000013   KARUPPUSAMY S A, UMASANGEETHA S, NANDHAGOPAL N (ECE Dep, Excel Engineering Coll, Namakkal, Tamilnadu) : Study on intelligent naive Bayesian probabilistic estimation practice for traffic flow to form stable clustering in VANET. Int J Info Computing Sci 2019, 6(2), 91-7.

The Vehicular Ad hoc Network (VANET) is one of the promising and encouraging technologies, and it is going to attract great attention in the near future. VANET has turned into a main module of the intelligent transport system. It is a self-controlled, wheeled network, and a wider and stimulating class of Mobile Ad hoc Network (MANET). VANET’s raise many innovative challenges because of their high – class and unique features, such as high-node mobility dynamic topology changes, wireless links breakage, network constancy, and network scalability. A well – organized routing protocol is one of the challenging matters of such networks. In this project an intelligent naive Bayesian probabilistic estimation practice for traffic flow to form a stable clustering in VANET, is proposed. The proposed scheme aims to improve routing by employing awareness of the current traffic flow as well as considering the blend of several factors, such as speed difference, direction, connectivity level, and node distance from its neighbors by using the intelligent technique. The proposed technique has proven to be more strong, stable, robust, and scalable than existing ones.

4 illus, 15 ref

000022   MIRI S K, SAHU N (IT Dep, Dr. C. V Raman Univ, Bilaspur, Chhattisgarh, Email: neelamsahu16@gmail.com) : Design and development of HSES knowledge portal. Int J Info Computing Sci 2019, 6(2), 85-90.

HSES stands Higher Secondary Education Sector which is backbone of backbone of the Indian education. Syllabus, question papers, e-Books and video lectures for classes XI &XII are major facilities of this educational web portal. The URL of HSES Knowledge Portal is www.hseshiksha.in. An online repository of various learning resources is called the knowledge portal. To design and develop a knowledge portal, we need open source software, knowledge and technical skill. PHP, HTMP, MySQL and web server are used in this portal. The admin user manages this portal. The students, teachers and others are three verities of the portal users. Feedback and suggestion can be given by the registered users of our portal. Implementation facility is reserved for the admin. It helps the students and teachers to learn and access anything easily. It also helps other users who want to help the learner of school level. HSES KP reduces the requirement of money and time-consuming. A quality based research is gaining through it. It facilitates tacit and explicit knowledge for users to support members of the institutions or organizations in all aspects of their learning, teaching, working, research and other activities. It helps the learner for downloading and accessing anything easily. Discussion forum is a group of registered members of the KP-HSES. The member of discussion forum will help to improve the portal. This portal promotes green education.

8 illus, 1 table, 10 ref

000011   JHA A, JOEL M R, MERCY W (CSE Dep, SMK Fomra Institute of Technology, Tamil Nadu - 603 103, Email: abhinayjha222@gmail.com) : Study on COSKQ generalizing for location seekers. Int J Info Computing Sci 2019, 6(2), 69-73.

This study analysis gives a crystal clear idea about the research concept over the collective spatial keyword query. The main aim of study is to collect some spatial keyword query for the contiguous keyword penetration. The current world enjoys with smart devices in its smartness, popularity and more functionality applications in people’s daily lives are continuously increasing. To save the time to search the basic searched queries like hospitals, restaurants, road networks, locations etc., the collective spatial keyword queries penetration services can be useful and interoperable way .

13 ref

000008   BHARNE P K , PRABHUNE S S (Computer Science & Engg. Dep, Shri Sant Gajanan Maharaj Coll of Engg., Shegaon, Maharashtra, Email: pankajbharne@gmail.com) : Stock market prediction system using machine learning technique. Int J Info Computing Sci 2019, 6(2), 53-8.

Stock market decision - making is a very difficult task to predict financial data. Stock market prediction with high precision the movement of return benefits for stock investors. Due to the complexity of the financial data of the stock market, the development of efficient models for prediction decision is very difficult, and must be precise. This study attempted to develop models for stock market prediction and to decide whether to buy or hold stocks using machine learning techniques. In this paper we propose an combination of Artificial Neural Network (ANN) and Fuzzy Neural Network (FNN) algorithm for implementing the prediction models. The Swarm intelligent technique, Particle Swarm Optimization algorithm (PSO) is also used to develop a forecasting model for predication of stock market. The stock market dataset and online websites dataset used as input to the prediction model. Based on the data set, these models are able to generate purchase / retention signal for the stock market as output. The main objective of this project is to predict the gainer or looser shares.

1 illus, 10 ref

000027   PRIYA M B, JAYASANKARI S (Computer Science Dep, P.K.R Arts Coll for Women (Autonomous), Gobichettipalayam – 638 452) : A complete analysis of K-SVD_DWT algorithm for improvising the PSNR ratio in image denoising. Int J Info Computing Sci 2019, 6(2), 35-52.

The important component of the developing countries economic growth is agriculture. The eminence of the crop is solely based on the plant‟s growth and the plant‟s involvement is exceedingly imperative for the surrounding as well as human life. Like humans, the plants also endure from diseases. The numerous varieties of diseases affect the plants and its growth. The parts of the plant like stem, bud, leaf or the entire plant may get affected by this type of diseases. The plant may die when this problem is not effectually identified and treated. Hence, some sort of disease diagnosis is required to recognize the disease. In this work, leaf disease detection problem is taken and resolved by image processing methods. There are several procedures for analyzing, identifying and classifying the leaf disease. The process includes pre-processing of an image, image segmentation, feature extraction and classification. In the projected work, the denoising technique is examined for the leaf disease detection and it can be made effectual by performing denoising phenomena which is known to be noise reduction technique. This feature can be executed by incorporating the method called K-SVD_DWT which improves the speed, accuracy and PSNR ratio when compared to the existing techniques. The K-SVD is the singular value decomposition and the generalized view of k-means clustering for indicating the signal from the group of signals and helps to obtain a dictionary to estimate every signal with a meager permutation of the atoms. The Peak Signal to Noise Ratio (PSNR) of the Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and K-SVD_DWT are compared and the result proves its effectiveness.

5 illus, 5 tables, 11 ref

000029   SHARMA K, SAMBYAL A (Computer Science & Engineering Dep, Sri Sai Univ, Himachal Pradesh - 176 081, Email: kanikasagar004@gmail.com) : Sentiment analysis using amazon data for WDE-KNN algorithm. Int J Info Computing Sci 2019, 6(2), 1-9.

Any kind of attitude, through or judgment that occurs due to any feeling is known as a sentiment which is also known as opinion mining. The sentiments analysis is the technique which is applied on analyses sentiment of the input data. The sentiments of individuals towards particular elements are analyzed in this approach. To gather sentiment information, web or internet is the best known source. In comparison to several other domains, the sentiment analysis requires higher analysis studies. This research work is based on the sentiment analysis of product reviews of Amazon data. To apply sentiment analysis the technique of feature extraction and classification is applied. The subjective view of a product review reflects the opinions expressed by opinion words, while the objective view is constructed by the remaining text features. For the sentiment analysis in the previous work, the WDE-LSTM technique is applied and which is replaced with the WDE-KNN technique. In the proposed method every product review has two views: subjective view and objective view. The existing and proposed techniques are implemented in python and simulation results shows that accuracy of the proposed technique is better than previous. The simulation results shows that execution time of the proposed method is less as compared to existing method.

4 illus, 1 table, 17 ref

000014   KEYNES J K D, PUNITHAVATHANI D S (Computer Science & Engineering Dep, Manonmaniam Sundaranar Univ, Tirunelveli, Tamil Nadu, Email: deepak.keynes@gmail.com) : SEEC- structured energy efficient clustering with bi-fold cluster head selection in WSN. Int J Info Computing Sci 2019, 6(1), 150-65.

In Wireless Sensor Networks (WSN), there is a greater research scope in energy efficient clustering for improving the life time of the network and also concerning about the load balancing among sensor nodes. Moreover, clustering is an effectual method for minimizing the energy consumption of the overall network. Since there are some energy limitations in battery depended sensor nodes and also some location based load balancing issues due to hot spotting, clustering has always been a crucial technique in WSN. The properties of energy heterogeneity of the network nature should be considered on designing a clustering model. With those concerns, a novel Structured Energy Efficient Clustering (SEEC) with uneven cluster size in the situation of producing diffusive energy on each sensor node has been proposed in this paper. For optimizing the energy consumption, the proposed model combines the multiple factors in the size of clusters and cluster numbers effectively. Moreover, the process of Cluster Head selection has been implemented through Bi-fold Cluster Head Selection (BCHS) model. In BCHS, the selection process works under two stages. First phase involves in selecting the temporary cluster head selection based on the sensor node‟s initial and lasting energy levels. Following that, in the second phase, the temporary cluster heads are replaced with an efficient CH among the defined cluster members and final list of cluster heads are formed. By employing this model, it is to be ensured that nodes having higher energy receive more chances for being CHs. Furthermore, the adduced model provides well distributed consumption of energy that obviously increases the longevity of network lifetime. Experiential results are analyzed with various factors such as throughput, packet delivery ratio, end-to-end delay and energy consumption. And, evaluated results re compared with the existing methodologies like LEACH, HEED and so on to provide the evidence for the efficiency of the proposed work.

11 illus, 1 table, 19 ref

000028   SAKHI G, BALASUBRAMANIAN R, NEDUNCHEZHIAN R (Computer Science and Engineering Dep, Manonmaniam Sundaranar Univ, Tirunelveli - 627 012, Email: rbalus662002@yahoo.com) : Convolutional neural networks for high spatial resolution remote sensing image classification. Int J Info Computing Sci 2019, 6(1), 138-49.

Hyperspectral remote sensing based image classification is found to be a very widely used method employed for scene analysis that is from a remote sensing data which is of a high spatial resolution. Classification is a critical task in the processing of remote sensing. On the basis of the fact that there are different materials with reflections in a particular spectral band, all the traditional pixel-wise classifiers both identify and also classify all materials on the basis of their spectral curves (or pixels). Owing to the dimensionality of the remote sensing data of high spatial resolution along with a limited number of labelled samples, a remote sensing image of a high spatial resolution tends to suffer from something known as the Hughes phenomenon which can pose a serious problem. In order to overcome such a small-sample problem, there are several methods of learning like the Support Vector Machine (SVM) along with the other methods that are kernel based and these were introduced recently for a remote sensing classification of the image and this has shown a good performance. For the purpose of this work, an SVM along with Radial Basis Function (RBF) method was proposed. But, a feature learning approach for the classification of the hyperspectral image is based on the Convolutional Neural Networks (CNNs). The results of the experiment that were based on various image datasets that were hyperspectral which implies that the method proposed will be able to achieve a better performance of classification compared to other traditional methods like the SVM and the RBF kernel and also all conventional methods based on deep learning (CNN).

4 illus, 1 table, 18 ref

000035   VIJAYALAKSHMI K, KALAISELVAN R (Computer Science and Engineering Dep, Srinivasa Subbaraya Government Polytechnic Coll, Puthur - 609 108, Email: mailtovijius@gmail.com) : The performance of software defined networks modeling and simulation. Int J Info Computing Sci 2019, 6(1), 130-7.

Software-defined networking (SDN) technology is an approach to cloud computing that facilitates network management and enables programmatically efficient network configuration in order to improve network performance and monitoring. Software defined networking offers numerous benefits including on-demand provisioning, automated load balancing, streamlined physical infrastructure and the ability to scale network resources in lockstep with application and data needs. Software Defined Networking (SDN) has emerged as a new networking paradigm for managing different kinds of networks ranging from enterprise to home net-work through software enabled control. In this paper we enhance the Modeling a system involves the abstraction of its features and properties, focusing exclusively on those that are of interest to the study. As a result, a model can be understood as the logical representation of a system with different levels of complexity (normally less complex than the real system). Simulation is the imitation of a real-world system through a computational representation of its behavior according to the rules described previously in a model. When a system is simulated, it is mandatory to consider a limited number of characteristics, properties or behaviors of interest, so as to make the model tractable; otherwise, it will be infinitely more complex and detailed. We further identify some challenges and promising future directions on SDN security, compatibility and scalability issues that should be addressed in this field.

10 illus, 20 ref

000030   SHIVANI, KATOCH M (Computer Science Dep, Sri Sai Univ, Himachal Pradesh - 176 081) : The novel scheme for isolation of malicious nodes from mobile ad-hoc networks. Int J Info Computing Sci 2019, 6(1), 113-22.

In MANET different mobile nodes are connected through wireless link. MANET distinctive mobiles are linked through wireless link every mobile are loose to transport i.e. no crucial controller to be had. In MANETs, collection of cellular nodes may dynamically vary the topological shape. With recognize to the extra extensively used ad Hoc Networks do not use any shape of constant infrastructure or centralized administration those forms of networks have the salient characteristics: dynamic topologies, bandwidth constraints, variable capability hyperlinks, confined bodily safety and electricity –confined operations. There are different types of attacks in MANET. Sinkhole and Wormhole attacks is one of the sorts, in this type of attacks node transfer from other course in place of path assigned to supply and vacation spot. So misplaced of statistics is viable. . In this research work, the novel scheme is proposed which detect and isolation malicious nodes from the network. The proposed technique detect and isolate the attacker node using different techniques such as watchdog technique, which detect the malicious nodes by using ICMP. The malicious nodes are responsible to trigger wormhole and sinkhole attack in the network. The proposed technique is carried out in NS2 and simulation outcomes suggests that proposed technique performs properly in comparison to different techniques.

4 illus, 1 table, 14 ref

000016   KHUMANE D D, JAGADE S M (Electronics and Telecommunication Engineering Dep, S.T.B. Coll of Engineering, Tuljapur, Maharashtra, Email: smjagade@gmail.com) : Direction of arrival estimation using a dynamic-MUSIC algorithm. Int J Info Computing Sci 2019, 6(1), 64-8.

The quality of mobile communication should be improved, however it is difficult to maintain the quality of mobile communication at certain locations because of multipath fading, delay spreading and cochannel interference. In order to cope with these problems, transmitters of the interference waves are estimated and it is desirable to improve the area design. An array antenna system with innovative signal processing can enhance the resolution of a signal direction of arrival (DOA) estimation. Super resolution algorithms take advantage of array antenna structures to better process the incoming signals. They also have the ability to identify multiple targets. This paper explores the eigen-analysis category of super resolution algorithm. A class of MUltiple Signal Classification (MUSIC) algorithms known as a Dynamic-MUSIC algorithm is presented in this paper. We focus on the dynamic environment of user i.e. user moves from his initial position to particular location. And by using Dynamic-MUSIC algorithm estimate their correct position location (PL) to provide the services to the desired user using extended version of MUSIC.

7 illus, 2 tables, 10 ref

000018   KULKARNI N, CHAUDHARI N S , TANWANI S (Indian Institute of Technology, Indore, Madhya Pradesh, Email: nsc0183@gmail.com) : Privacy in LBS: Dilemma vs realities. Int J Info Computing Sci 2019, 6(1), 47-51.

In this information age, with ubiquitous computing we leave trail of our digital usage everywhere, putting privacy at stake. Privacy preserving is beyond doubt an identified priority today and multiple research initiatives have been undertaken to address it. However, the casual attitude of users and providers in handling the information revealed during the use of Location Based Services (LBS) raises a serious concern over privacy. The existing approaches to preserve privacy seem inadequate. In this paper, the core concerns regarding LBS privacy, possible attacks and the effectiveness of approaches are addressed. Finally, open challenges that need immediate attention in the given domain are discussed.

1 illus, 4 tables, 8 ref

000025   PANDE A V, THAKARE Y A (Computer Science and Engineering Dep, Sant Gadge Baba Amravati Univ, Maharashtra - 444 602, Email: ankita.pande2@gmail.com) : A new approach to solve Knapsack Problem. Int J Info Computing Sci 2019, 6(1), 1-8.

The Knapsack Problem belongs to a large class of problems known as Optimization Problem. This problem is to maximize the obtained profit without exceeding the knapsack capacity. It is a very special case of the well-known Integer Linear Programming Problem. The purpose of this paper is to analyze several feasible solutions to a Fractional Knapsack Problem using greedy approach. Based on the knapsack algorithm to take different feasible solutions, in this set of feasible solutions, particular solution that satisfies the objective of the function. Such solution is called optimum solution. The optimum selection is without revising previously generated solutions. The greedy choices are made one after the other, reducing each given problem instance to smaller one. The greedy choices bring efficiency in solving the problem with the help of sub problems.

6 ref

000032   SINGH V P, GUPTA S, PASUPULETI H, BABU N S C (Centre for Development of Advanced Computing, Karnataka - 560 038, Email: svaibhav@cdac.in) : A methodology to study the effect of smoke and fire on indoor RF propagation. J Inst Eng India Ser B 2019, 100(1), 33–9.

An indoor wireless network comprises of multiple devices connected wirelessly in a small geographical area, giving mobility to the connected devices. RF signal associated with wireless networks suffers from many propagation losses. It becomes essential to estimate losses at different locations, in the intended coverage area, to attain the desired performance seamlessly. It helps in the optimal device placement in the wireless networks. In the application, in addition to the propagation losses, there is a need to estimate the loss induced by the presence of smoke and fire, individually, in an indoor environment. This paper first deals with the design of a heatmap generating tool, in MATLAB, which gives the approximate signal strength at different locations on a given floor for a given transmitter. Then the paper discusses the formation of plasma in fast flaming fires and presents the path loss due to it. This path loss is incorporated in the heatmap generating tool and a heatmap is generated for our office ground floor for the case of a flaming fire. The paper also describes a testbed, setup in our office premises, to study the scattering loss caused by smoke particles.

6 illus, 4 tables, 11 ref

000010   BRIJAWI A , SAMRAN A, SAMRAN A, ALQERBAN A, MURAD M (Restorative and Prosthetic Dental Sciences Dep, Dar Al Uloom Univ, Riyadh- 13314, Saudi Arabia, Email: asamran@dau.edu.sa) : Effect of different core design made of computer‑aided design/computer‑aided manufacturing system and veneering technique on the fracture resistance of zirconia crowns: A laboratory study. J Conserv Dent 2019, 22(1), 59-63.

It is unclear how the different core designs made of computer‑aided design/computer‑aided manufacturing (CAD/ CAM) system and veneering techniques affect the fracture resistance of endodontically treated teeth. The aim of this in vitro study is to investigate the effect of different core designs made of CAD/CAM system and veneering techniques on the fracture resistance of zirconia ceramic crowns. Two types of zirconia copings were designed; the first one with circumferential 0.5 mm chamfer and the second one with circumferential 1 mm deep chamfer. The core specimens (in subgroups) were veneered anatomically with either a layering technique (hand‑layer) or with press‑on technique resulting in four test groups (n = 12). All crowns were then cemented using self‑adhesive resin cement. After that, all specimens were loaded in a universal testing machine until fractured. Data were then analyzed with two‑way analysis of variance (ANOVA) (α = 0.05). Mean (standard deviation) failure loads for groups ranged from 2412.7 N (± 624.6) to 3020.1 N (± 1099.8). Two‑way ANOVA revealed no statistically significant differences among groups (P > 0.05). Almost all groups showed cohesive failure in the veneering ceramic. Within the limitations of this laboratory study, neither the core design nor the veneering technique affected the fracture resistance of all‑ceramic crowns significantly.

2 illus, 2 tables, 25 ref

000019   LEE H-Y, CHEN Y-J, LI C-C, LI W-M, HSU Y-L, YEH H-C, KE H-L, HUANG C-N, LI C-F, WU W-J, KUO P-L (Urology Dep, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan, Email: wejewu@kmu.edu.tw) : Deduction of novel genes potentially involved in upper tract urothelial carcinoma using next-generation sequencing and bioinformatics approaches. Int J Med Sci 2019, 16(1), 93-105.

Upper tract urothelial carcinoma (UTUC) is a relatively uncommon cancer worldwide, however it accounts for approximately 30 % of urothelial cancer in the Taiwanese population. The aim of the current study is to identify differential molecular signatures and novel miRNA regulations in UTUC, using next-generation sequencing and bioinformatics approaches. Two pairs of UTUC tumor and non-tumor tissues were collected during surgical resection, and RNAs extracted for deep sequencing. There were 317 differentially expressed genes identified in UTUC tissues, and the systematic bioinformatics analyses indicated dysregulated genes were enriched in biological processes related to aberration in cell cycle and matrisome-related genes. Additionally, 15 candidate genes with potential miRNA-mRNA interactions were identified. Using the clinical outcome prediction database, low expression of SLIT3 was found to be a prognostic predictor of poor survival in urothelial cancer, and a novel miRNA, miR-34a-5p, was a potential regulator of SLIT3, which may infer the potential role of miR-34a-5p-SLIT3 regulation in the altered tumor microenvironment in UTUC. Our findings suggested novel miRNA target with SLIT3 regulation exerts potential prognostic value in UTUC, and future investigation is necessary to explore the role of SLIT3 in the tumor development and progression of UTUC.

11 illus, 3 tables, 72 ref

026206   SANYAL P, GANGULI P, BARUI S, DEB P (Pathology Dep, Command Hospital (EC), Kolkata, West Bengal, Email: prosenjitganguli@hotmail.com) : Pilot study of an open-source image analysis software for automated screening of conventional cervical smears. J Cytol 2018, 35(2), 71-4.

The Pap stained cervical smear is a screening tool for cervical cancer. Commercial systems are used for automated screening of liquid based cervical smears. However, there is no image analysis software used for conventional cervical smears. The aim of this study was to develop and test the diagnostic accuracy of a software for analysis of conventional smears. The software was developed using Python programming language and open source libraries. It was standardized with images from Bethesda Interobserver Reproducibility Project. One hundred and thirty images from smears which were reported Negative for Intraepithelial Lesion or Malignancy (NILM), and 45 images where some abnormality has been reported, were collected from the archives of the hospital. The software was then tested on the images. The software was able to segregate images based on overall nuclear: cytoplasmic ratio, coefficient of variation (CV) in nuclear size, nuclear membrane irregularity, and clustering. 68.88 % of abnormal images were flagged by the software, as well as 19.23 % of NILM images. The major difficulties faced were segmentation of overlapping cell clusters and separation of neutrophils. The software shows potential as a screening tool for conventional cervical smears; however, further refinement in technique is required.

2 illus, 1 table, 15 ref

026204   DAS A A, JACOB E (CSIR-NIIST, Thiruvananthapuram - 695 019, Email: liz.csir@gmail.com) : In silico prediction of ErbB signal activation from receptor expression profiles through a data analytics pipeline. J Biosci 2018, 43(2), 295–306.

The ErbB signalling pathway has been studied extensively owing to its role in normal physiology and its dysregulation in cancer. Reverse engineering by mathematical models use the reductionist approach to characterize the network components. For an emergent, system-level view of the network, we propose a data analytics pipeline that can learn from the data generated by reverse engineering and use it to re-engineer the system with an agent-based approach. Data from a kinetic model that estimates the parameters by fitting to experiments on cell lines, were encoded into rules, for the interactions of the molecular species (agents) involved in biochemical reactions. The agent model, a digital representation of the cell line system, tracks the activation of ErbB1-3 receptors on binding with ligands, resulting in their dimerization, phosphorylation, trafficking and stimulation of downstream signalling through P13-Akt and Erk pathways. The analytics pipeline has been used to mechanistically link HER expression profile to receptor dimerization and activation of downstream signalling pathways. When applied to drug studies, the efficacy of a drug can be investigated in silico. The anti-tumour activity of Pertuzumab, a monoclonal antibody that inhibits HER2 dimerization, was simulated by blocking 80 % of the cellular HER2 available, to observe the effect on signal activation.

8 illus, 3 tables, 31 ref

026207   SAXENA V L, CHATURVEDI P, FATIMA K (Zoology Dep, D.G.P.G. Coll, Kanpur- 208001, Email: vijayashokkanpur@gmail.com) : In silico identification of microrna (miRNAs) and their target prediction from Colorado tick fever virus from complete genome. Asian J Exp Sci 2018, 32(1), 45-50.

MicroRNAs (miRNAs) newly identified class of non-protein-coding RNA is ~20nt small RNAs which play an important roles in multiple biological process by de-gradating target mRNAs or repressing mRNA translation. It affects the gene regulation by affecting base pairing of the messenger RNA. Its misregulation may be linked with cancer. MicroRNAs (miRNAs) bind to complementary sequences on the 3' untranslated region (UTR) of mRNAs from hundreds of target genes, leading either to mRNA degradation or suppression of translation. Computational prediction is analyzed and estimation of evolutionary relationship among types of organism is done wih 14 precursors and eight potential miRNAs. miRNAs target 86 target sitein 8 gene PEG3, ZIM2, MIDN1, TTN, C16orf57, LRRC27, ABCA1.

4 tables, 17 ref

026205   GONI I, GUMPY J M, ZIRA P B (Computer Science Dep, Adamawa State Univ, Mubi, Nigeria) : Designing a fuzzy rule based system to predict students academic performance in Adamawa State University Mubi. Arch Appl Sci Res 2018, 10(2), 28-35.

This research work is for the design of fuzzy logic based expert system to predict student academic performance in Adamawa State University Mubi; Fuzzy Inference System (FIS) is used in this work, Gaussian membership function is used in this research work; the described experiments were implemented on MATLAB environment 12. Some candidates were used to test the system. The results of the findings reveal that Student academic performance can be predicted based on the inputs used, the system shows 90 % accuracy.

9 illus, 8 ref

026203   SHAILAJA A, BINDU B B V, SRINATH M, GIRI C C (Osmania Univ, Hyderabad- 500 007, Telangana, Email: giriccin@yahoo.co.in) : In silico structural and functional analysis of copalyl diphosphate synthase enzyme in Andrographis paniculata (Burm. f.) Wall. ex Nees: A plant of immense pharmaceutical value. Ann Phytomed 2018, 7(1), 69-77.

Andrographis paniculata (Burm. f.) Wall. ex Nees (Acanthaceae) with immense medicinal importance lacks information on its biosynthetic pathway genes and their regulatory role in the production of pharmaceutically important andrographolide. Copalyl diphosphate synthase (CPS) is involved in the production of copalyl diphosphate, a precursor for many bioactive compounds with particular reference to diterpene lactone. In this study, we elucidated the structural and functional aspects of A. paniculata CPS (ApCPS). Composition of amino acids and hydrophobicnature of ApCPS were analysed and identified as non trans-membrane protein. A chloroplast transit peptide and mitochondrial targeting peptide in ApCPS were identified. Protein secondary structure prediction has given insight on the distribution of helix (52.52 %), loop (45.91 %) and strands (1.56 %) in ApCPS. The homology modelling of ApCPS was carried out with SWISS MODEL.The validation of 3D model using PROCHECK revealed that 91.74 % of the residues have averaged 3D-1D score >= 0.2 which is structurally reliable. In Ramachandran plot, 90.9 % amino acid residues were found in most favoured region. Phylogenetic tree was constructed using MEGA 7.0by taking eudicots, monocots, gymnosperms and fungal species. Among them, ApCPS was clustered within eudicots and closely related to Sesumum indicum in Laminales. Protein-protein interaction study using STRING10 revealed that CPS interacts with gibberilic acid and terpene synthase related proteins. In Arabidopsis thaliana, CPS coexpression was seen with gibberelic acid related proteins.The present in silico analysis will be useful in understanding the structural, functional and evolutionary diversification of ApCPS.

20 illus, 46 ref

026202   KOLISNYK T Y , RUBAN O A , FIL N Y , KUTSENKO S A (Industrial Technology of Drugs Dep, National Univ of Pharmacy, Kharkiv, Ukraine, Email: kolisnyktatyana@gmail.com) : Application of an artificial neural network for design of sustained-release matrix tablets containing Vaccinium myrtillus leaf powder extract. Asian J Pharm 2018, 12(2), 137-45.

Vaccinium myrtillus leaf extracts are promising source of natural remedies for diabetes mellitus Type 2 management and prevention. The aim of this study was to design the sustained-release matrix tablets containing V. myrtillus leaf powder extract with the application of an artificial neural network (ANN). The amounts of Methocel K4M, Methocel K100LV, and Eudragit L100 were used as input factors affecting the release from matrix tablets. Each input factor was varied on three levels according to Box–Behnken design. The in vitro percent release at time points of 2, 8, and 16 h were used as output datain training, testing, and validating the neural network. The software Matlab was used to create an ANN and the number of nodes in the hidden layer was selected based on trial and error approach to develop a model with the best predictive ability. The multilayer perceptron with one hidden layer was constructed. The network with nine nodes in the hidden layer was used to simulate in vitro release from hypothetical formulations and the matrix forming agent ratio was selected by the brute-force method. The dissolution profile for the selected formulation of matrix tablets was studied. The evaluation of the release kinetics and mechanism indicated a coupling of the diffusion and erosion as release mechanisms. ANNs can be successfully applied to develop herbal sustained release matrix tablets.

5 illus, 3 tables, 27 ref

026201   VYAS M, THAKUR S, RIYAZ B, BANSAL K K, TOMAR B, MISHRA V (Ayurveda Dep, Lovely Professional Univ, Phagwara, Punjab, Email: vijaymishra2@gmail.com) : Artificial intelligence: The beginning of a new era in pharmacy profession. Asian J Pharm 2018, 12(2), 72-6.

Artificial intelligence (AI) is a branch of computer science that deals with the problem-solving by the aid of symbolic programming. It has greatly evolved into a science of problem-solving with huge applications in business, health care, and engineering. One of the pivotal applications of AI is the development of the expert system. With the advent of big data and AI, robots are now becoming more trust worthy for doctors, and a large number of institutions are now employing robots along with human supervision to carry out activities that were previously done by humans. The major advantage of AI is that it reduces the time that is needed for drug development and, in turn, it reduces the costs that are associated with drug development, enhances the returns on investment and may even cause a decrease in cost for the end user. A large number of researches are being carried out to improve the current available AI technology to make the pharmacy profession more efficient. The present article briefly describes the importance of AI in the process of drug development and then looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.

25 ref

028673   BACHHAWAT N (1079/2, HIG Complex, Chandigarh - 160 036, Email: nanditabachhawat45@gmail.com) : PE-only/PE_PGRS proteins of Mycobacterium tuberculosis contain a conserved tetra-peptide sequence DEVS/DXXS that is a potential caspase-3 cleavage motif. J Biosci 2018, 43(4), 597–604.

Mycobacterium tuberculosis H37Rv is an intracellular pathogen responsible for causing tuberculosis in humans. The M. tuberculosis genome has been shown to contain a very large and unique family of PE proteins made of two sub-families: PE-only and PE_PGRS proteins. These two subtypes of proteins play a crucial role in the pathogenesis of the microbe. However, despite numerous investigations, the role of these proteins in disease development remains obscure. In this study, sequence analysis with a search for short conserved motifs revealed a conserved tetra-peptide motif DEVS/DXXS at the PE domain of almost every PE-only and PE_PGRS protein. The motif was found at a distance of 43–46 amino acids from the N-terminal of PE_PGRS proteins, and at a distance of between 35 and 82 amino acids of the PE-only proteins. As phosphorylation of the serine residue of this tetra-peptide could yield a motif similar to the caspase-3 binding recognition sequence DEVD/E, the region from a representative PE_PGRS protein (PE_PGRS45) was docked to human caspase-3. Strong interactions of only the protein containing the phosphorylated motif (DEVpS/DXXpS) to caspase-3 were observed. This suggested that the conserved DEVS/DXXS motif could have evolved for phosphorylation and subsequent recognition by caspase-3. These findings have important implications in unravelling the role of these PE proteins in mycobacterial infection.

3 tables, 44 ref

029923   CHAUDHARI A J, KULKARNI V, SAHOO N (Indian Institute of Technology Guwahati, Guwahati - 781 039, Email: shock@iitg.ernet.in) : State-of-the-art technology in variable compression ratio mechanism for spark ignition engine. Sadhana 2018, 43(12), 211.

Present investigations deal with development of a novel variable compression ratio (VCR) mechanism and its implementation in a small and relatively large size single-cylinder engines. Operation of this mechanism is found to be smooth and effective in the running condition of the engine as well. This mechanism, when incorporated in the small size spark ignition HONDA engine, portrayed improvement in engine performance with increment in compression ratio (CR) for petrol and kerosene. Their respective optimum CRs 5.02 (petrol) and 5.27 (kerosene) are higher than the base value 4.8. In case of large size KIRLOSKAR engine, the present VCR mechanism is found to be useful while operating with liquefied petroleum gas (LPG), where measurements showed that combustion duration is lower with LPG for CR 9.79 as compared with base value 9.0. The present experiments clearly demonstrate the usefulness of VCR mechanism in improving engine performance for a given fuel and broadening the range of alternative fuels burnt in the engine. Ease of fabrication, simplicity in installation, accessibility in troubleshooting and smooth run-time alterations are the advantages with the current novel mechanism.

15 illus, 4 tables, 36 ref

029927   PHULL D K, KUMAR G B (VIT Univ, Chennai - 600 127, Email: dkphull@gmail.com) : Ameliorated language modelling for lecture speech recognition of Indian English. Sadhana 2018, 43(12), 209.

A great amount of research is growing towards the automatic transcription of lectures that consist of numerous information and knowledge that could be helpful to the educational systems and institutes. In large vocabulary speech recognition, language model plays a paramount role in reducing the humongous search space. However, language modelling is very brittle when moving from one domain to another or when moving from read speech to spontaneous speech. Also, lecture speech recognition will have some of the characteristics of spontaneous speech. Hence, it is very challenging to build the language model for this task. In this paper, a judicious approach to adapt the language model in a way where the language model will be in close proximity to the topic spoken in the lecture speech has been depicted. The evaluation of the language model is devised using the proposed approach with the existing language models such as CMU Sphinx, Gigaword and HUB-4. We observed the results analysis that the language models devised from the proposed approach outperform from the existing language models in terms of word error rate, perplexity and out of vocabulary rate. Analysis shows that the presented two-phase approach has resulted in an average decrease of the word error rate to be approximately 14% and the perplexity is decreased by half on average.

5 illus, 6 tables, 34 ref

029931   VAIRAM T, SARATHAMBEKAI S, UMAMAHESWARI K (Information Technology Dep, PSG Coll, Coimbatore - 641 004, Email: tvairam@gmail.com) : Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization. Sadhana 2018, 43(12), 206.

Task Scheduling is a complex combinatorial optimization problem and known to be an NP hard. It is an important challenging issue in multiprocessor computing systems. Discrete Particle Swarm Optimization (DPSO) is a newly developed swarm intelligence technique for solving discrete optimization problems efficiently. In DPSO, each particle should limit its communication with the previous best solution and the best solutions of its neighbors. This learning restriction may reduce the diversity of the algorithm and also the possibility of occurring premature convergence problem. In order to address these issues, the proposed work presents a hybrid version of DPSO which is a combination of DPSO and Cyber Swarm Algorithm (CSA). The efficiency of the proposed algorithm is evaluated based on a set of benchmark instances and the performance criteria such as makespan, mean flow time and reliability cost.

10 illus, 9 tables, 24 ref

029924   KUMAR M, JINDAL M K, SHARMA R K, JINDAL S R (Computational Sciences Dep, Maharaja Ranjit Singh Punjab Technical Univ, Bathinda, Punjab, Email: munishcse@gmail.com) : A novel framework for writer identification based on pre-segmented Gurmukhi characters. Sadhana 2018, 43(12), 197.

Handwriting is an obtained apparatus utilized for correspondence of one’s recognition or sentiments. Components that judge a person’s handwriting is not merely subject to the individual’s handwriting depends on the background, additionally considers like nervousness, inspiration and the reason for the handwriting. In spite of the high variation, in a man’s handwriting, recent outcomes from various writers have demonstrated that it has adequate individual quality to be utilized as an identification strategy. In this paper, the authors are the pact with a novel approach to text dependent writer identification in view of pre-segmented Gurmukhi characters. The text dependent writer identification framework proposed in this paper includes distinctive stages like preprocessing, feature extraction, classification or identification. The feature extraction stage incorporates four schemes, zoning, diagonal, transitions and peak extent based features. To analyze the proposed framework execution, experiments are performed with two classifiers, namely, k-NN and SVM. SVM is also considered with linear-kernel in the present work. For experimental results, we have collected 31,500 samples from 90 different writers for 35 class problem. Maximum writer identification accuracy of 89.85 % has been achieved by using a combination of zoning, transition and peak extent based features with Linear-SVM classifier when we have taken 70 % data as the training set and remaining 30 % data as the testing set. Using 10-fold cross validation, we have achieved an accuracy of 94.76 % with a combination of zoning, transition and peak extent based features and Linear-SVM classifier.

4 illus, 5 tables, 25 ref

029930   SINGH H, SHARMA R K, SINGH V P (Computer Science and Engineering Dep, Thapar Institute of Engineering & Technology (Deemed Univ), Patiala, Email: harjeet.singh@thapar.edu) : Recognition of online unconstrained handwritten Gurmukhi characters based on finite state automata. Sadhana 2018, 43(11), 192.

This paper presents a language-based efficient post-processing algorithm for the recognition of online unconstrained handwritten Gurmukhi characters. A total of 93 stroke classes have been identified to recognize the Gurmukhi character set in this work. Support Vector Machine (SVM) classifier has been employed for stroke classification. The main objective of this paper is to improve the character level recognition accuracy using an efficient Finite State Automata (FSA)-based formation of Gurmukhi characters algorithm. A database of 21,945 online handwritten Gurmukhi words is primarily collected in this experiment. After analysing the collected database, we have observed that a character can be written using one or more strokes. Therefore, a total of 65,946 strokes have been annotated using the 93 identified stroke classes. Among these strokes, 15,069 stroke samples are considered for training the classifier. The proposed system achieved promising recognition accuracy of 97.3 % for Gurmukhi characters, when tested with a new database of 8,200 characters, written by 20 different writers.

14 illus, 8 tables, 29 ref

029925   LAKSHMI K, VISALAKSHI N K, SHANTHI S (Computer Applications Dep, Kongu Engineering Coll, Perundurai, Email: klakshmisanthosh@gmail.com) : Data clustering using K-Means based on Crow Search Algorithm. Sadhana 2018, 43(11), 190.

Cluster analysis is one of the popular data mining techniques and it is defined as the process of grouping similar data. K-Means is one of the clustering algorithms to cluster the numerical data. The features of K-Means clustering algorithm are easy to implement and it is efficient to handle large amounts of data. The major problem with K-Means is the selection of initial centroids. It selects the initial centroids randomly and it leads to a local optimum solution. Recently, nature-inspired optimization algorithms are combined with clustering algorithms to obtain the global optimum solution. Crow Search Algorithm (CSA) is a new populationbased metaheuristic optimization algorithm. This algorithm is based on the intelligent behaviour of the crows. In this paper, CSA is combined with the K-Means clustering algorithm to obtain the global optimum solution. Experiments are conducted on benchmark datasets and the results are compared to those from various clustering algorithms and optimization-based clustering algorithms. Also the results are evaluated with internal, external and statistical experiments to prove the efficiency of the proposed algorithm.

13 illus, 14 tables, 42 ref

029928   RAY P, CHAKRABARTI A, GANGULI B, DAS P K (Computer Science Dep, Dinabandhu Andrews Institute of Technology and Management, Kolkata - 700 094, Email: rayparamita@yahoo.com) : Demonetization and its aftermath: an analysis based on twitter sentiments. Sadhana 2018, 43(11), 186.

Sentiment analysis has become a very useful tool in recent times for studying people’s opinions, sentiments and subjective evaluation of any event of social and economic relevance, and in particular, policy decisions. The present paper proposes a framework for sentiment analysis using twitter data for the ’demonetization’ effort of the Government of India. The paper employs twitter data using Twitter API. The methodology of the paper involves collection of data from twitter from different cities of India using geolocation and preprocessing followed by a lexicon-based approach to analyse users’ sentiments over a period of five weeks preceding the policy announcement. In addition to this, the paper also attempts to analyse the sentiments of specific groups of people representing diverse interest groups.

13 illus, 11 tables, 45 ref

029926   MALHOTRA J, BAKAL J (Computer Science and Engineering Dep, G. H. Raisoni College of Engineering, Nagpur - 440 016, Email: jyotijmalhotra1@gmail.com) : Second Order Mutual Information based Grey Wolf Optimization for effective storage and de-duplication. Sadhana 2018, 43(11), 185.

This paper intends to perform de-duplication for enhancing the storage optimization by utilizing the similarity in mutual information. Hence, this paper contributes by proposing a hybrid fingerprint extracting using SH and HC algorithms. Secondly, the data is clustered using the latest technique called as SOMI-GO to extract the metadata. The extracted metadata is stored in metadata server which provides better storage optimization and de-duplication. SOMI-GO is adopted as it provides maximum second-order mutual information based on the similarity index. The proposed SOMI-GO technique is compared with the existing methods such as K-means, K-mode, ED-PSO, ED-GA and ED-GWO in terms of accuracy, TPR, TNR and performance time and the significance of the SOMI-GO method is described.

8 illus, 2 table, 54 ref

029922   BHAVYA M, RAMYA M, NAGARJUN N, AMRESH N, SATHYAMURTHY B (Biochemistry Dep, Ramaiah Coll of Arts, Science and Commerce, Bangalore, Karnataka) : Docking study of selected Vitis vinifera seeds constituents on dengue viral proteins: An in silico approach. J Med Plants Stud 2018, 6(6), 100-5.

Dengue is a mosquito-borne systemic viral infection caused by any of the four antigenically related dengue viruses (DENV). The dengue virus belongs to the Flaviviridae family of viruses that cause diseases in humans. A virtual screening analysis of phytochemical structures with dengue virus protein targets has been carried out using a molecular docking approach with Vins vinifera seeds. Grapes (Vitis vinifera) are believed to have health benefits due to their antioxidant activity and polyphenols. In this study we examined the binding affinities of 14 ligands with seven non structural Dengu viral proteins through in silico methods like virtual screening and docking process which showed that compound F and compound N had high binding efficiencies with these proteins along with the type of hydrogen bonds and their respective amino acid residues at their docked sites.

7 illus, 3 tables, 16 ref

029929   SANTHANALAKSHMI P, OOMMEN S, ALWAR M C, ARYA J (Pharmacology Dep, Pondicherry Institute of Medical Sciences, Puducherry, Email: santhanalakshmibalaji@gmail.com) : Effectiveness of computer-assisted learning as a teaching method in experimental pharmacology. Natl J Physiol Pharm Pharmacol 2018, 8(11), 1470-4.

Recently, there has been a progressive reduction in the use of animals for teaching purpose due to ethical consideration. Computer-assisted learning (CAL) is one of the non-animal alternatives in experimental pharmacology to simulate the live experiment using animals. Although it offers benefits of being reproducible, time saving, and having minimum errors, it has its own drawbacks. The present study is undertaken to study the effectiveness of CAL by comparing demonstration method using live animals and CAL method among 2nd year MBBS students. A total of 71 students participated in the study. They were given a set of multiple-choice questions (MCQs) on a selected topic (effect of diazepam on mice using Rotarod apparatus) after demonstration of experiment using animals. Later, CAL was performed by all the students followed by the same set of MCQs. A student feedback questionnaire based on the 5-point Likert scale was also given to all the students to get their opinion about the simulation experiments. The students had a better average score in CAL method as compared to the method using animals (82.4 % vs. 44.6 %). Based on feedback, majority of students (70 %) agreed in favor of CAL. The effectiveness of CAL in teaching experimental pharmacology has been demonstrated and students agreed that CAL assisted them in understanding the topic better as the effects were visualized on the screen clearly. Thereby, such simulations should be considered as an essential component of the standard curriculum.

1 illus, 1 table, 17 ref

028677   GOKCE S, CETIN A, KIBAR R (Physics Dep, Manisa Celal Bayar Univ, Manisa 45140, Turkey, Email: sibel.gokce@cbu.edu.tr) : Investigating pedestrian evacuation using ant algorithms. Pramana - J Phys 2018, 91(5), 62.

Ants communicate with each other by depositing a chemical called pheromone on the substrate while they crawl forward. By this way, they follow their predecessor and large trail systems are built. Inspired by the communication via chemical signals of ants, we have proposed a model to investigate the collective motion in humans during an emergency. It is considered that pedestrians use some kind of virtual chemotaxis to find the shortest way to the exit. This basic idea is implemented with the floor field model which is the most popular cellular automata model. The dependence of the evacuation from a room on the virtual chemotaxis evaporating rate f and the presence of the obstacle are investigated in this paper. The simulation results show that the increase in evaporation rate has been seen to slow down the evacuation. Moreover, it is found that positioning the obstacles in the room could lead to the phase transitions and decrease the evacuation time.

13 illus, 72 ref

028676   GAJJAR S, SARKAR M, DASGUPTA K, CHANIYARA D (Computer Science and Engineering Dep, Nirma Univ, Ahmedabad - 382481, Email: sachin.gajjar@nirmauni.ac.in) : Low energy fuzzy based unequal clustering multihop architecture for wireless sensor networks. Proc Natl Acad Sci India A 2018, 88(4), 539–56.

This paper presents a Low Energy Fuzzy based Unequal Clustering Multihop Architecture (LEFUCMA) for wireless sensor networks consisting of several nodes that send sensed data to a Master Station (MS). LEFUCMA encompasses neighbor finding, cluster head selection, clustering and routing protocols. The neighbor finding protocol organizes the network into a sectored-layers structure. The cluster head selection uses fuzzy logic with residual energy, number of neighboring nodes, packet reception rate and distance of node from MS as fuzzy descriptors for cluster head selection. For even distribution of traffic, LEFUCMA uses fuzzy logic with node density and distance of area from MS as fuzzy descriptors to decide number of cluster heads in a given area. To avoid hot spots problem, LEFUCMA uses an unequal clustering mechanism with clusters closer to MS having smaller sizes than those farther from MS. Finally, inter-cluster routing protocol decides the next hop cluster head considering its residual energy, distance from MS and from current cluster head which represents energy required for communication, number of cluster members which represents intra-cluster traffic and number of descendant nodes which represents inter-cluster traffic. A comparative analysis of LEFUCMA; Unequal Hybrid, Energy Efficient and Distributed Clustering (Ever et al. in Proceedings of international conference on sensor networks, pp 185–193, 2012; Energy Aware Distributed Unequal Clustering (Yu et al. in Hindawi Int J Distrib Sens Netw, Article ID 202145:1–8, 2011); Constructing Optimal Clustering Architecture (Li et al. in J Comput Commun 36(3):256–268, 2013; and Energy Aware Unequal Clustering using Fuzzy logic (EAUCF) (Bagci and Yazici in J Appl Soft Comput 13(4):1741–1749, 2013) shows that LEFUCMA is 32–42 % more energy efficient compared to EAUCF. Throughput of LEFUCMA is 46 % more and network lifetime is 60–75 % more compared to EAUCF.

8 illus, 3 tables, 57 ref

028678   GOUR A, PARDASANI K R (Bioinformatics Dep, Maulana Azad National Institute of Technology, Bhopal - 462051, Email: alekh_g@yahoo.co.in) : Soft fuzzy set approach for mining frequent amino acid associations in peptide sequences of dengue virus. Proc Natl Acad Sci India A 2018, 88(4), 529–38.

Association analysis of amino acids in molecular sequences can reveal crucial information and knowledge for understanding structure, function and interaction of proteins. The traditional methods of association rule mining like apriori, F-P Growth etc. fail to generate appropriate patterns due to inherent uncertainty present in data. The uncertainty in sequence data caused by variation in the length of sequences and lack of parameterization lead to under prediction and over prediction of the results. In this paper an attempt has been made to develop a soft set based approach for mining fuzzy association patterns in peptide sequences of dengue virus. The fuzzy set approach is employed to incorporate the degree of relationships among amino acids due to variation in length of the sequences. The soft set approach is employed to incorporate the relationship of parameters with amino acid association patterns. The 12,581 sequences of dengue virus are downloaded from NCBI and screened for redundancy to obtain non redundant 6995 sequences. The amino acid associations are explored and analyzed using soft fuzzy approach. Also the results obtained by soft fuzzy approach are compared with the results obtained individually by ordinary, fuzzy and soft set approaches. The soft fuzzy approach is able to overcome the issue of under prediction and over prediction of the results obtained by other approaches. Also the interesting association rules have been generated to predict the structure and physico chemical properties of the peptide sequences.

1 illus, 4 tables, 30 ref

028675   CHOKESHAIUSAHA K, SANANMUANG T, PUTHIER D, NGUYEN C (Veterinary Science Dep, Rajamangala Univ of Technology Tawan-OK, Chonburi, Thailand, Email: kaj.chk@gmail.com) : An innovative approach to predict immune-associated genes mutually targeted by cow and human milk microRNAs expression profiles. Vet World 2018, 11(9), 1203-9.

Milk is rich in miRNAs - the endogenous small non-coding RNA responsible for gene post-transcriptional silencing. Milk miRNAs were previously evidenced to affect consumer’s immune response. While most studies relied on a few well characterized milk miRNAs to relate their immunoregulatory roles on target genes among mammals, this study introduced a procedure to predict the target genes based on overall milk miRNA expression profiles - the miRNome data of cow and human. Cow and human milk miRNome expression datasets of cow and human milk lipids at 2, 4, and 6 months of lactation periods were preprocessed and predicted for their target genes using TargetScanHuman. Enrichment analysis was performed using target genes to extract the immune-associated gene ontology (GO) terms shared between the two species. The genes within these terms with more than 50 different miRNAs of each species targeting were selected and reviewed for their immunological functions. Results: A total of 146 and 129 miRNAs were identified in cow and human milk with several miRNAs reproduced from other previous reports. Enrichment analysis revealed nine immune-related GO terms shared between cow and human (adjusted p ≤ 0.01). There were 14 genes related to these terms with more than 50 miRNA genes of each species targeting them. These genes were evidenced for their major roles in lymphocyte stimulation and differentiation. A novel procedure to determine mutual immune-associated genes targeted by milk miRNAs was demonstrated using cow and human milk miRNome data. As far as we know, this was the 1st time that milk miRNA target genes had been identified based on such cross-species approach. Hopefully, the introduced strategy should hereby facilitate a variety of cross-species miRNA studies in the future.

3 illus, 3 tables, 48 ref

028674   CHAUDHARY R K, SINGH G, NARAIAN R, RAM S (Gautam Buddha Univ, Greater Noida - 201 312, Email: s.ram@gbu.ac.in) : Structural and functional in-silico analysis of toxin-antitoxin proteins in persister cells of Pseudomonas aeruginosa. Plant Arch 2018, 18(2), 1643-51.

Pseudomonas aeruginosa is an opportunistic as well as one of the most challenging hospitals and community-acquired pathogen producing toxin-antitoxin (TA). The production of TA is thought to regulate the multidrug tolerance and pathogenicity in bacterial pathogens. Recently, it has been well recognized that TA systems play a very crucial role in the formation of persister cells, which leads to recurrent chronic infections. Toxin-antitoxin proteins interact with RNA and protein molecules present in bacterial cell, which, consequently, halts the normal cellular process by inhibiting the molecules involved in transcription and translation as well as in other metabolic pathways. In this study, we have extensively assessed the homology modeling, protein interaction and functional relationship, along with the active site of TA proteins of P. aeruginosa. Our results represent the secondary structures of all TA proteins, which were highly conserved, and their sequence identity was between 88 to 100 % through the BLASTp. Additionally, we compared the three dimensional (3D) models for all TA proteins through homology modeling that identified the HigA, HigB, ParD and ParE proteins as good models. Finally, the CASTp was utilized to identify the active site, which is generally specific for the binding of toxin and DNA molecules. The study suggests that all TA proteins found in P. aeruginosa have great potential function and responsible for antimicrobial drug tolerance and pathogenicity

6 illus, 1 table, 42 ref

027436   KUMAR S, KUMAR T V V (Jawaharlal Nehru Univ, New Delhi, Email: tvvijaykumar@hotmail.com) : A novel quantum-inspired evolutionary view selection algorithm. Sadhana 2018, 43(10), 166.

A data warehouse (DW) is designed primarily to meet the informational needs of an organization’s decision support system. Most queries posed on such systems are analytical in nature. These queries are long and complex, and are posed in an exploratory and ad-hoc manner. The response time of these queries is high when processed directly against a continuously growing DW. In order to reduce this time, materialized views are used as an alternative. It is infeasible to materialize all views due to storage space constraints. Further, optimal view selection is an NP-Complete problem. Alternately, a subset of views, from amongst all possible views, needs to be selected that improves the response time for analytical queries. In this paper, a quantum-inspired evolutionary view selection algorithm (QIEVSA) that selects Top-K views from a multidimensional lattice has been proposed. Experimental comparison of QIEVSA with other evolutionary view selection algorithms shows that QIEVSA is able to select Top-K views that are comparatively better in reducing the response times for analytical queries. This in turn aids in efficient decision making.

40 illus, 107 ref

027445   SHOME S K, MUKHERJEE A, KARMAKAR P, DATTA U (Academy of Scientific and Innovative Research (AcSIR), Durgapur - 713 209, Email: saikatkumarshome@gmail.com) : Adaptive feed-forward controller of piezoelectric actuator for micro/nano-positioning. Sadhana 2018, 43(10), 158.

In this paper, design and implementation of an adaptive feed-forward controller for micro/nanopositioning control of piezoelectric actuator (PEA) is described. Discrete-time Dahl hysteresis-based mathematical model of the PEA is developed and the values of the model parameters are estimated through an autoregressive with exogenous terms (ARX) model identification technique using experimental input–output data. A recursive least-square estimator (RLSE)-based adaptive feed-forward (FF) controller is proposed, which takes into account the parameter uncertainty. The FF controller has also been implemented in a DsPIC30F4011 microcontroller. The established PEA model and the controller are validated by simulation and experimental results including parameter variation.

9 illus, 3 tables, 41 ref

027448   THENMOZHI D, ARAVINDAN C (Computer Science and Engineering Dep, SSN Engineering Coll, Kalavakkam - 603 110, Email: theni_d@ssn.edu.in) : Ontology-based Tamil-English cross-lingual information retrieval system. Sadhana 2018, 43(10), 157.

Cross-lingual information retrieval (CLIR) systems facilitate users to query for information in one language and retrieve relevant documents in another language. In general, CLIR systems translate query in source language to target language and retrieve documents in target language based on the keywords present in the translated query. However, the presence of ambiguity in source and translated queries reduces the performance of the system. Ontology can be used to address this problem. The current approaches to ontology-based CLIR systems use manually constructed multilingual ontology, which is expensive. However, many methods exist to automatically construct ontology for any domain in English but not in other languages like Tamil. We propose a methodology for Tamil–English CLIR system by translating the Tamil query to English and retrieve pages in English to address these issues. Our approach uses a word sense disambiguation module to resolve the ambiguity in Tamil query. An automatically constructed ontology in English is used to address the ambiguity of English query. We have developed a morphological analyser for Tamil language, Tamil–English bilingual dictionary and named entity database to translate a Tamil query to English. The translated query is reformulated using ontology and the reformulated queries are given to a search engine to retrieve English documents from the Internet. We have evaluated our methodology for agriculture domain and the evaluation results show that our approach outperforms other approaches in terms of precision.

2 illus, 16 tables, 31 ref

027450   ZHAO H, WANG C (Tongling Univ, Tongling 244061, Anhui Province, People’s Republic of China, Email: happyzhaohaibo@126.com) : A new adaptive control of dual-motor driving servo system with backlash nonlinearity. Sadhana 2018, 43(10), 155.

In order to weaken the influence of backlash nonlinearity on a dual-motor driving servo system, we first establish the state-space model of the system. We then propose a new adaptive controller combining a projection algorithm with backstepping control for the first time, to the best of our knowledge, and analyze its stability. In the simulation analysis, we respectively choose a triangular wave, sawtooth wave, and random signal as the input signal. Simulation results validate a higher tracking accuracy and stronger adaptability of the proposed control law than that of mere backstepping control. In the experimental tests, we respectively choose a step signal and sine signal and simultaneously apply a white noise signal to the system output after 3 s in each test. The test results validate a stronger adaptability and robustness than that of mere backstepping control.

21 illus, 33 ref

027439   PADI S, MURTHY H A (Indian Institute of Technology Madras, Chennai - 600 036, Email: padi.sarala@gmail.com) : Segmentation of continuous audio recordings of Carnatic music concerts into items for archival. Sadhana 2018, 43(10), 154.

Concert recordings of Carnatic music are often continuous and unsegmented. At present, these recordings are manually segmented into items for making CDs. The objective of this paper is to develop algorithms that segment continuous concert recordings into items using applause as a cue. Owing to the ‘here and now’ nature of applauses, the number of applauses exceeds the number of items in the concert. This results in a concert being fragmented into different segments. In the first part of the paper, applause locations are identified using time, and spectral domain features, namely, short-time energy, zero-crossing rate, spectral flux and spectral entropy. In the second part, inter-applause segments are merged if they belong to the same item. The main component of every item in a concert is a composition. A composition is characterised by an ensemble of vocal (or main instrument), violin (optional) and percussion. Inter-applause segments are classified into three segments, namely, vocal solo, violin solo, composition and thaniavarthanam using tonic normalised cent filterbank cepstral coefficients. Adjacent composition segments are merged into a single item, if they belong to the same melody. Meta-data corresponding to the concert in terms of items, available from listeners, are matched to the segmented audio. The applauses are further classified based on strength using Cumulative Sum. The location of the top three highlights of every concert is documented. The performance of the proposed approaches to applause identification, inter-applause classification and mapping of items is evaluated on 50 live recordings of Carnatic music concerts. The applause identification accuracy is 99 %, and the inter- and intra-item classification is 93 %, while the mapping accuracy is 95 %.

21 illus, 12 tables, 29 ref

027433   ANANTHAPADMANABHA T V, GIRISH K V V, RAMAKRISHNAN A G (Indian Institute of Science, Bangalore, Email: vijay.girish@gmail.com) : Relative occurrences and difference of extrema for detection of transitions between broad phonetic classes. Sadhana 2018, 43(10), 153.

Detection of transitions between broad phonetic classes in a speech signal has applications such as landmark detection and segmentation. The proposed hierarchical method detects silence to non-silence transitions, sonorant to non-sonorant transitions and vice-versa. The subset of the extrema (minimum or maximum amplitude samples) above a threshold, occurring between every pair of successive zero-crossings, is selected from each frame of the bandpass-filtered speech signal. Locations of the first and the last extrema lie on either side far away from the mid-point (reference) of a frame, if the speech signal belongs to a non-transition segment; else, one of these locations lies within a few samples from the reference, indicating a transition frame. The transitions are detected from the entire TIMIT database for clean speech and 93.6 % of them are within a tolerance of 20 ms from the phone boundaries. Sonorant, unvoiced non-sonorant and silence classes and their respective onsets are detected with an accuracy of about 83.5 % for the same tolerance with respect to the labelled TIMIT database as reference. The results are as good as, and in some aspects better than, the state-ofthe-art methods for similar tasks. The proposed method is also tested on the test set of the TIMIT database for robustness with respect to white, babble and Schroeder noise, and about 90 % of the transitions are detected within a tolerance of 20 ms at the signal to noise ratio of 5 dB. On NTIMIT database, 62.7 % of the transitions are detected, and 63.5 % of the sonorant onsets, within 20 ms tolerance.

12 illus, 7 tables, 29 ref

027432   AKHIL P T, SUNDARESAN R (Electrical Communication Engineering Dep, Indian Institute of Science, Bangalore - 560 012, Email: akhilpt@ece.iisc.ernet.in) : Algorithms for separable convex optimization with linear ascending constraints. Sadhana 2018, 43(9), 146.

The paper considers the minimization of a separable convex function subject to linear ascending constraints. The problem arises as the core optimization in several resource allocation scenarios, and is a special case of an optimization of a separable convex function over the bases of a polymatroid with a certain structure. The paper generalizes a prior algorithm to a wider class of separable convex objective functions that need not be smooth or strictly convex. The paper also summarizes the state-of-the-art algorithms that solve this optimization problem. When the objective function is a so-called d-separable function, a simpler linear time algorithm solves the problem.

4 illus, 1 table, 25 ref

027442   SAMANI Z R, MOGHADDAM M E (Shahid Beheshti Univ, Tehran 1983969411, Iran, Email: m_moghadam@sbu.ac.ir) : A multi-criteria context-sensitive approach for social image collection summarization. Sadhana 2018, 43(9), 143.

Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features. The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identify attractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.

10 illus, 2 tables, 38 ref

027447   TEMBHURNE O, SHRIMANKAR D (Computer Science and Engineering Dep, Visvesvaraya National Institute of Technology, Nagpur - 440 010, Email: owtembhurne@gmail.com) : N-PSO: Endmember extraction using advance particle swarm optimization for NLMM. Sadhana 2018, 43(9), 141.

This paper presents a fully unsupervised endmember extraction technique for hyperspectral image unmixing using nonlinear mixing model. The underlying idea of the model is that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive noise. These nonlinear functions are approximated using polynomial functions, leading to a polynomial post-nonlinear mixing model (PPNM). In an unknown environment, the evaluation of the parameters involved in PPNM model is a tedious task, which is categorized as an NP hard problem. A method based on the combination of swarm intelligence, least-square (LS) and sub-gradient-based optimization (SO) is proposed to estimate the parameters involved in the model. The particle swarm optimization (PSO) is used to search the optimal endmember combination in the feasible solution space. The nonlinearity and respective abundances are evaluated using the LS and SO method, respectively. The proposed method is equipped with an adaptive tuning parameter-free mechanism and modified updating strategy. This strategy not only improves the result in terms of overall accuracy but also maintains physical constraints on the value of the resultant endmember set. The proposed method has been evaluated using simulated and real hyperspectral scenes. The experimental results on the hyperspectral scenes show that the proposed method obtains a higher extraction precision than those of the existing endmember extraction algorithms. Statistical analysis on a real hyperspectral image shows that the results obtain using N-PSO are 20–40 % better than those from the existing approaches.

23 illus, 7 tables, 39 ref

027449   UZUNHISARCIKLI E, GOREKE V (Erciyes Univ, Kayseri, Turkey, Email: uzunhise@erciyes.edu.tr) : A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system. Sadhana 2018, 43(9), 138.

Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90 % biopsy possibility (BI-RADS-4) and with 2 % biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a twolayer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34 % success rate in benign/malignant classification and a 92 % success rate in category classification (BIRADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.

8 illus, 9 tables, 84 ref

027440   PAREEK G, PURUSHOTHAMA B R (Computer Science and Engineering Dep, National Institute of Technology Goa, Farmagudi, Ponda 403401, Email: gpareek@nitgoa.ac.in) : Provably secure group key management scheme based on proxy re-encryption with constant public bulletin size and key derivation time. Sadhana 2018, 43(9), 137.

Users share a group key to decrypt encryptions for the group using a group key management scheme. In this paper, we propose a re-encryption-based group key management scheme, which uses a unidirectional proxy re-encryption scheme with special properties to enable group members share the updated group key with minimum storage and computation overhead. In particular, we propose a proxy re-encryption scheme that supports direct re-encryption key derivation using intermediate re-encryption keys. Unlike multihop re-encryption, the proposed proxy re-encryption scheme does not involve repeated re-encryption of the message. All the computations are done on the re-encryption key level and only one re-encryption is sufficient for making the group key available to the users. The proposed scheme is the first for group key management based on proxy re-encryption that is secure against collusion. The individual users store just one individual secret key with group key derivation requiring O(logN) computation steps for a group of N users. Size of the public bulletin maintained to facilitate access to the most recent group key for off-line members is O(N) and remains constant with respect to the number of group updates. The proposed group key management scheme confronts attacks by a non-member and even a collusion attack under standard cryptographic assumptions.

6 illus, 1 table, 25 ref