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Bioinformatics is an active research area which combines biological matter as well as computer science research. Detection of disease causing human Deoxyribo Nucleic Acid (DNA) sequence analysis is one of the major application areas under bioinformatics. Among the severe diseases, the number of Dengue cases and deaths are raised in Tamil Nadu. Identification of sequence motifs involved in Dengue virus is essential for early prediction and saving human life. It includes wide ranges of steps for disease diagnosing. The scope of this proposed work is to provide the longest common subsequence which present in a normal and Dengue virus affected human DNA sequence. The human DNA sequences are collected from National Center for Biotechnology Information (NCBI) database. Human DNA sequence is separated as k-mer using k-mer separation rule. From that, the separated k-mers are clustered using Self Organizing Map (SOM) algorithm. In which mean, median and standard deviation are used as features for clustering k-mers. Then obtained k-mers clusters are given to the Longest Common Subsequence (LCSS) algorithm to find common subsequence with higher length, which presents in every k-mers clusters. Time consumption for identification of LCSS is compared for both normal and Dengue virus affected DNA.

Bioinformatics, K-mers, Longest Common Sub Sequence (LCSS), String pattern matching algorithms

[1] Vinayak Majki, Sudip Paul and Rachna Jain, “Bioinformatics for Healthcare Applictions”, IEEE Conference, pp.2014-207, 2019.
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[3] Izzat Alsmadi and Maryam Nuser, “String Matching Evaluation Methods for DNA Comparison”, International Journal of Advanced Science and Technology”, Vol.47, pp.13-32, 2012.
[4] Sasikala S, Ratha Jeyalakshmi T, “Extensive Review on Computational Predictions of Genomic Regulatory Sequences”, International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.91-94, 2019.
[5] Amit U Sinha and Raj Bhatnagar, “Efficient and Scalable Motif Discovery using Graph-based Search”, IEEE symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp.197-204, 2007.
[6] Khumukcham Robindro, Ashoke Das, “Effectiveness of Ssaha Algorithm for Searching Motif in Large Databases of DNA Sequences” , International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.79-87, 2017.
[7] S.Rajesh, S.Prathima and Dr.L.S.S.Reddy, “Unusual Pattern Detection in DNA Database using KMP Algorithm”, International Journal of Computer Applications (0975-8887), Vol.1, Issue.22, pp.1-5, 2010.
[8] Benjamin Schuster-Bockler and Alex Bateman, “Protein interactions in human genetic diseases”, Genome Biology, Vol.9, Issue 1, Article R9, pp.R9.1-R9.12, 2008.
[9] Chein-Hung Huang, Huai Shun Peng and KA-Lok Ng, “Prediction of Cancer Proteins by Integrating Protein Interaction, Domain frequency and Domain Interaction Data using Machine Learning Algorithms”, BioMed Research International, Vol.2015,pp.1-10, 2015.
[10] Lei Yang, Xudong Zhao and Xianglong Tang, “Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network”, International Journal of Biological Sciences, Vol.10, Issue.7, pp.677-688, 2014.
[11] Pankaj Bhanbri, O.P. Gupta, “Phylogenetic Tree Construction for Distance based Methods”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.142-149, 2017.
[12] Sumedha S.Gunawardena, “Optimum-time, Optimum-space, Algorithms for k-mer Analysis of Whole Genome Sequences”, Journal of Bioinformatics and Comparative Genomics, Vol.1, pp.1-12, 2014.
[13] Teuvo Kohonen and Panu Somervuo, “Self-organizing maps of symbol strings”, Elsevier, Neurocomputing 21, pp.19-30, 1998.
[14] Marghny Mohamed, Abeer A. Al-Mehdhar, Mohamed Bamatraf and Moheb R.Girgis, “Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification”, Intelligent Information Management, Vol.5, pp.25-33, 2013.
[15] Dr.S.A.M.Rizvi and Pankaj Agarwal, “A New Bucket-Based Algorithm for Finding LCS from two given Molecular Sequences”, IEEE, Third International Conference on Information Technology: New Generations, 2006.
[16] Xuyu Xiang, Dafang Zhang and Jiaohua Qin, “A New Algorithm for the Longest Common Subsequence Problem”, IEEE, International Conference on Computational Intelligence and Security Workshops, pp.112-115, 2007.
[17] Coasts S. Iliopoulos and M. Sohel Rahman, “Algorithms for Computing Variants of the Longest Common Subsequence Problem”, Elsevier – Theoretical Computer Science, pp.255-267, 2008.

G. Tamilpavai, C. Vishnuppriya, "Detection of Longest Common Sub Sequence in Normal DNA and Dengue Virus Affected Human DNA using Self Organizing Map," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.1-7, 2020.

This paper describes a new image watermarking method for which is suitable for both copyright protection and information hiding. The presented method is based upon the morphological mathematics properties of the Mojette Transform [1,2] and the Mojette Phantoms[3]. The main properties of the Mojette transform are briefly introduced and the concept of linked phantoms which depicts the null space of the operator is presented. In this paper the Mojette Phantoms can be used not only as the embedded watermark, but also can be used as the mark which is inscribed with some certain information, e.g. Chinese characters. Corresponding embedding and extractions of either the mark or the hidden message are then described. Finally, experimental results are presented in the last section.

Mojette, Watermarking, Transform, Hiding, Image

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[9] Hewa Majeed Zangana, “Watermarking System Using LSB”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 19, Issue 3, Ver. II, P. 75-79, (May.-June. 2017).
[10] Harshal V. Patil1, B. H. Barhate2, "A Review Paper on Data Hiding Techniques: Stegnography", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.64-67, 2018
[11] Manisha Verma, Hardeep Singh Saini, "Analysis of Various Techniques for Audio Steganography in Data Security", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.1-5, 2019

Hewa Majeed Zangana, "Watermarking Image Depending on Mojette Transform for Hiding Information," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.8-12, 2020.

Iris recognition is considered as one of the reliable technique in biometric system to gain higher security. In this paper research is focusing on an efficient iris recognition technique. Iris of an eye image is segmented, unwrapped into a rectangular strip and normalized. Normalized iris is transformed into polar coordinate and filtered. A mask is applied for noise suppression and encoded using encoding technique. This encoded iris pattern features are extracted and template is generated. This final template is stored in the database and input image template pattern is matched using pattern matching technique. This experiment uses two standard database images CASIA V1.0 and IITD, the performance measure FAR and FRR for different threshold values is considered for the evaluation of the system.

FAR, FRR, Feature Extraction, Wavelets Transform

[1] Sudha Gupta, Asst. Professor ,LMIETE, LMISTE, Viral Doshi, Abhinav Jain and Sreeram Iyer, K.J.S.C.E. Mumbai India,”Iris Recognition System using Biometric Template Matching Technology”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 12, pp. 3075-3083 2017© Research India Publications. http://www.ripublication.com
[2] Aditya Nigam, Lovish, Amit Bendale and Phalguni Gupta, “Efficient Iris Recognition System Using Relational Measures” Department of Computer Science and Engineering, Indian Institute of Technology Kanpur Kanpur 208016, INDIA fnaditya,lovishc,bendale,pgg@cse.iitk.ac.in
[3] Sunil Swamilingappa Harakannanavar1, C. R. Prashanth2, Vidyashree Kanabur3, Veena I. Puranikmath4 and K. B. Raja5,” An Extensive Study of Issues, Challenges and Achievements in Iris Recognition ” Asian Journal of Electrical Sciences ISSN: 2249-6297, Vol. 8, No. 1, pp. 25-35, 2019 © The Research Publication, www.trp.org.in
[4] Shailender Kumar1, Krishnanand Mishra2 and Rahul Vashisht3, ” Iris Recognition Based on Unique Iris Templates for Reliable Personal Authentication”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 12 pp. 3075-3083, 2017 © Research India Publications. http://www.ripublication.com
[5] Mohammed A. M. Abdullah, F. H. A. Al-Dulaimi, Waleed Al-Nuaimy & Ali Al-Aataby, “Efficient Small Template Iris Recognition System Using Wavelet Transform” International Journal of Biometrics and Bioinformatics (IJBB), Volume (5): Issue (1) 16.
[6] Sudha Gupta, Viral Doshi, Abhinav Jain and Sreeram Iyer,”Iris Recognition System using Biometric Template Matching Technology” © International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 2, 2010
[7] Humayan Kabir Rana1, Md. Shafiul Azam2, Mst. Rashida Akhtar3 3 , Julian M.W. Quinn4, and Mohammad Ali Moni5, “A fast iris recognition system through 1 optimum feature extraction”, PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27363v3 | CC BY 4.0 Open Access | rec: 23 Feb 2019, publ: 23 Feb 2019.
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[9] H. Proença, A. Alexandre. “Towards noncooperative iris recognition: A classification approach using multiple signatures”. IEEE Transaction on Pattern Analysis, 29(4): 607-.612, 2007.
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A. A. Halder, S. R. Pande, "Implementation of Iris Recognition Using Circular Hough Transform and Template Generation," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.13-16, 2020.

Sepsis is one of the most common causes of morbidity and mortality in the Intensive Care Unit (ICU) patients. The lack of sensitive and specific clinical and laboratory variables for early identification of sepsis in critically ill patients is the causative factor for needless and delayed or untimely interruption of a proper antibiotic therapy. The current work developed a machine learning-based early sepsis prediction model in intensive care patients with vital parameters which evaluated the goodness of model fit and its accuracy. The predictors were extracted from combinations of vital sign measure and their changes over time. The dataset consisted of 20,336 patients (medical and surgical) who were admitted in ICU. Random Forest Classifier was used as the Machine learning algorithm for developing a predictive model. For the early prediction of sepsis in ICU patients, the Random Forest Classifier achieved an AUROC curve of 0.58 for the data collected from the patients within 24 hours. Sepsis is being the common cause of admission in ICU worldwide, a machine learning technique adopting statistical methods to conclude relationships between patient features and outcomes in large data set was successfully applied to predict adverse events.

MachineLearning, EarlySepsisprediction, Random Forest Classifier, AUROC

[1] V. Ribas, A.Vellido, J. Rodriguez, J. Rello, “Severe sepsis mortality prediction with logistic regression over latent factors”, Expert Systems with Applications, pp. 1937-1943, 2012.
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[4] S. Nemati, A. Holder, F. Razmi, M. Stanley, G. Clifford and T. Buchman, “An In- terpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU”, Critical Care Medicine, vol. 46, no. 4, pp. 547–553 ,2018.
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[6] K. Barajas, R. Akella, “Prediction of Pysiological Subsystem Failure and its Impact in the prediction of Patient Mortality”, In the Proceedings of IEEE, Internaltional Conference on Big Data, 2015.
[7] P. Thottakkara, T. Ozrazgat-Baslanti, B. Hupf, P. Rashidi, P. Pardalos, P. Momcilovic, A. Bihorac, “Application of Machine learning Techniques to High Dimensional Clinical to Forecast Postoperative Complications”, PLOS ONE, vol. 11, no. 5, 2016.
[8] S. Bhattacharya, V. Rajan, H. Shrivastava, “ICU Mortality Prediction: Classification Algorithm for Imbalanced Datasets”,In the Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), pp. 1228-1294, 2017.
[9] Soni, HK., “Machine Learning – A New Paradigm of AI”, International Journal of Scientific Rsearch in Network Security and Communication, pp. 31-32,2019
[10] Ghosh, S. & Waheed, Sajjad, “Strategic Analysis of classification algorithms for liver disease diagnosis”, Journal of Science Technology and Environment Informatics,pp. 361-370, 2017.

Aparna Shenoy, K.V. Viswanatha, Raju Ramakrishna Gondkar, "Early Sepsis Prediction in Intensive Care Patients using Random Forest Classifier," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.17-22, 2020.

In this proposed method, the segmentation of brain Magnetic Resonance Images (MRI) has been carried out using Pulse Coupled Neural network (PCNN) and classification by Back Propogation Neural Network (BPNN) techniques. The proposed method includes five stages pre-processing, clustering, feature extraction, feature selection and classification. For extracting the features Non Sub-sampled Contourlet Transform (NSCT) method has been used. For feature selection optimized Fire-fly intelligence has been preferred. Finally, the selected features are given to BPNN to identify the input data either as normal or abnormal. The performance of the classifier was evaluated in terms of True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) and the accuracy was found to be good.

PCNN, NSCT, Feature extraction, feature selection. Fire-fly, MR Brain Image

[1] D.Selvaraj, R., Dhanasekaran, A Review on Tissue Segmentation and Feature Extraction of MRI Brain Images, International journal of computer science & Engineering Technology,Vol. 4,pp. 1313-1332, 2013
[2] Iztok Fister, Iztok Fister Jr., Xin-She Yang, Janez Brest, A Comprehensive review of Firefly Algorithms, Swarm and Evolutionary Computation, Vol.13, pp. 34–46, 2013.
[3] Susana M., Vieira, Luís, F., Mendonça, Gonçalo J., Farinha, João M.C., and Sousa, Modified Binary PSO for Feature Selection using SVM Applied to Mortality Prediction of Septic Patients, Applied Soft Computing, Vol. 13, No. 8, pp. 3494–3504, 2013
[4] Ashish Kumar Bhandari, Vineet Kumar Singh, Anil Kumar, Girish Kumar ingh, Cuckoo Search Algorithm and Wind Driven Optimization based Study of Satellite Image Segmentation for Multilevel Thresholding using Kapur’s Entropy, Expert Systems with Applications, Vol. 41, No. 7, pp.3538–3560, 2014.
[5] Amita Kumari and Rajesh Mehra, Hybridized Classification of Brain MRI using PSO & SVM, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 3, No. 4, pp. 319-323, 2014.
[6] Abdenour, Mekhmoukhand Karim Mokrani, Improved Fuzzy C-Means Based Particle Swarm Optimization (PSO) Initialization and Outlier Rejection with Level Set Methods for MR Brain Image Segmentation,Computer Methods and Programs in Biomedicine,Vol. 122, No. 2, pp. 266-281, 2015.
[7] Nguyen Cong Long, Phayung Meesad, and Herwig Unger, A highly Accurate Firefly based Algorithm for Heart disease Prediction, Expert Systems with Applications, Vol. 42, No.21, pp.8221–8231, 2015.
[8] Kuntimad, G and Ranganath, H.S, Perfect Image Segmentation using Pulse Coupled Neural Networks, IEEE Transactions on neural networks, Vol. 10, No.3, pp. 591-598, 1999.
[9] Liu Xiaofang, ChengDansong, Tang Xianglong, Liu Jiafeng (2008), Image Segmentation based on Pulse Coupled Neural Network, IEEE Conference on information sciences,pp. 744-748.
[10] Jhon L. Johnson and Mary Lou Padgett, PCNN Models and Applications , IEEE Transactions on neural networks, Vol. 10,No. 3, pp. 480-498, 1999.
[11] B.Thamaraichelvi, G.Yamuna, Application of various segmentation techniques for brain mri –a comparative study, International Journal of Advances in Engineering & Technology, Vol. 7, Issue 5, pp. 1434-1442, 2014.
[12] He Xiaolan and Wu Yili, Texture Feature Extraction Method Combining Non- sub sampled Contour Transformation with Gray Level Co-Occurrence Matrix, Journal of multimedia, Vol. 8, No. 6, pp. 675-684, 2013.
[13] Xin-She Yang, Firefly Algorithms, Nature-Inspired Optimization Algorithms, Chapter 8, pp. 111-127, 2014.
[14] Vanita Jain, Aarushi Jain, Achin Jain, Arun Kumar Dubey, Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem, Research Paper | Isroset-Journal (IJSRCSE), Vol.6 , Issue.2 , pp.76-81, Apr-2018. doi: https://doi.org/10.26438/ijsrcse/v6i2.7681,
[15] G.R.Shahmohammadi, Kh.Mohammadi, Key Management in Hierarchical Sensor Networks Using Improved Evolutionary Algorithm, Research Paper | Journal (IJSRNSC), Vol.4 , Issue.2 , pp.5-14, Apr-2016.

B. Thamaraichelvi, "PCNN - Firefly Based Segmentation and Analysis of Brain MRI," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.23-29, 2020.

Landmark-based Spectral Clustering (LSC) is used for large scale spectral clustering. The basic idea of the our approach is designing an efficient way for graph construction. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure like distance functions. k-NN is a type of instance-based learning, or lazy learning. In this field, the CRF approach is relatively novel and considered a prominent choice as compared to other methods. However, a lot of scope for further enhancement of the CRF(Conditional Random Field) with Knn optimization problem. The Performance of CRF-Knn has shown quite significant resultsand using different datasets in this paper.The proposed technique improves the selection process using KNN algorithm. The results obtained show that the CRF found to be better than that of LSC in terms of Accuracy, time, recall and precision.

Landmark-based Spectral clustering, K nearest neighbors, CRF, Accuracy, time, precision and recall

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[18] Fadl Mutaher Ba-Alwi ,Houzifa M. Hintaya,” Comparative Study for Analysis the Prognostic in Hepatitis Data: Data Mining Approach”, International Journal of Scientific & Enginerring Research, Volume 4, 2013..
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Shivani Walia, P S Mann, "A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.30-39, 2020.

Big Data refers to a huge collection of data like the Banking data, social media data, repository data etc. These types of fields are responsible for day to day relevant data retrieval and processing. Clustering is one of major tasks which are done for data in order to minimize the time delay and efficient information retrieval. In this work we worked on similarity index in the form of cosine and soft cosine to count the total connection with respect to documents in the form of data. Then we use Cosine and Soft Cosine measures as hybrid Similarity algorithm to intakes the threshold policy of K means and co relation linkage property of Linkage clustering and forms new clusters. The cross-validation of the proposed work model has been done using Support Vector Machine followed by K-Mediod to improve the accuracy of clustering. This research work also focuses on different techniques of Clustering as well as classification. This research work mainly focuses on optimizing the clustering performance of the Big Data so that wealthy information can be retrieved with least cost.

Data mining, Big data, Clustering, Classification, Support Vector Machine

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[7] Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, and Samir Belfkih, “Big Data Technologies: A Survey”, Journal of King Saud University-Computer and Information Sciences, 2017.
[8] Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y. Zomaya, Sebti Foufou, and Abdelaziz Bouras, “A survey of clustering algorithms for big data: Taxonomy and empirical analysis”, IEEE transactions on emerging topics in computing, Vol. 2, no. 3, pp: 267-279, 2014
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[12] V. W. Ajin, and Lekshmy D. Kumar, "Big data and clustering algorithms", In IEEE International Conference on Research Advances in Integrated Navigation Systems (RAINS), pp. 1-5. 2016.
[13] Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, and Samir Belfkih, “Big Data Technologies: A Survey”, Journal of King Saud University-Computer and Information Sciences, 2017.

K. Sharma, P. Rehan, "A Hybrid Data Clustering Technique in Big Data using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.40-47, 2020.

This paper extracts and broadens rural teachers’ most effective proclaimed motivating strategies. From the data generated by 2 years of mixed method research in rural college, these strategies appeared as among the most victorious. Selection of best practices was formed on a synthesis of what both teachers and students reported as making the greatest positive impact on their colleges’ related motivation. Teaching strategies are illustrated by multiple exhaustive examples from teachers’ interviews. Strategies are i) start with your classroom set up, ii) make digital citizenship a priority, iii) teach mini-lessons before using devices, iv) use the power of choice, v) remember that sharing is caring, vi) conduct teacher check-ins etc. The teaching methodology is surveyed through the college. The surveyed data is plotted using histogram graph method using MATLAB platform. Histogram is a graphical display of data using bars of different heights. It is similar to a bar chart, but a histogram group’s number into ranges. The height of each bar shows how many fall into each range.

MATLAB, histogram, rural colleges, teaching strategies, motivation

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A. Sumathi, P. Nithyashankari, "Review of Best Teaching Methodologies in Rural Areas," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.48-52, 2020.

Cryptography is widely used technique to provide security in large networks like cloud environments. Classical cryptography and quantum cryptography are two widely used techniques. Classical cryptography uses simple mathematical methods to provide security. So it is more vulnerable to several attacks such as eaves dropping, man-in-the-middle attack etc. But in classical cryptography digital signatures are used to provide best authentication. Quantum cryptography uses quantum mechanical properties to provide security. It uses photons and polarization but it requires more communication rounds. So by combing classical and quantum cryptography to show a new combination. Here we can use implicit user authentication, explicit mutual authentication and we can digital signatures to provide best authentication. The main objectives of this article are design and develop an enhanced anonymity control scheme based on quantum cryptographic techniques. EANCTRL- an enhanced anonymity control scheme based on Quantum key distribution is presented in this article.

Quantum Computing, Cryptography, cloud computing, Anonymity in cloud

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V. Sreenija Reddy, Revathi A., Phani kumar N., "An Enhanced Anonymity Control Scheme based on Quantum Cryptography in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.53-56, 2020.

Modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Brain tumor is an abnormal mass of tissue in which some cells grow and multiply uncontrollably, apparently unregulated by the mechanisms that control normal cells. There are three types of tumor that are commonly observed viz. Benign, Pre-Malignant, and Malignant. Many supervised and unsupervised classification algorithms are used for detection of tumor as benign or malignant. Usually lighter datasets are used for image classification in application field where as comparatively larger and heavier datasets are used in case of medical field. Many parameters chosen during training play a very important role in measuring the performance and accuracy of the system. Thus an attempt has been made to clearly show how accuracy of the algorithm varies based on the parameters chosen for detection of brain tumor in human brain for an MRI image.

CNN, Transfer Learning, Medical Imaging, Glioma, Image Classification, Machine Learning, Deep Learning

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[4] MuhammedTalo, UlasBaranBaloglu, OzalYildirim, U RajendraAcharya “Application Of Deep Transfer Learning For Automated Brain Abnormality Classification Using MR Images”, IEEE, 2017
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Dheeraj D., Prasantha H.S., "Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.57-63, 2020.