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Development of prediction tools for computational identification of nitrogen fixation (nif) genes and categorization of potential diazotrophs using high throughput sequence data has accelerated the research in the area of biological nitrogen fixation. The computational tools are recently being used for the annotation and phylogenetic analysis of nifH gene or NifH protein sequences in nitrogen-fixing bacteria. In this study, phylogenetic analysis of NifH protein using Maximum Likelihood method showed that amino acid sequences of Azospirillum brasilense showed more relatedness to Rhodospirillum rubrum and Rhodobacter capsulatus. Further, the amino acid sequences also showed similarity to nodule-forming bacteria Rhizobium etli, Rhizobium leguminosarum bv. trifolii and Sinorhizobium meliloti. Azospirillum brasilense was placed on the same clade along with Rhodopseudomonas palustris, Methylobacterium nodulans, Gluconoacetobacter diazotrophicus and Zymomonas mobilis based on NifH aminoacid sequences. On another branch, NifH amino acid sequences of Azospirillum brasilense showed relatedness to Bradyrhizobium diazoefficiens, Azorhizobium claudinodans and Acidothiobacillus ferrooxidans. However, amino acid sequences of free-living nitrogen-fixing bacteria Klebsiella pneumoniae, Azotobacter vinelanii and Azotobacter chroococcum were also placed separately on other branch. Interestingly, sequences of anaerobic bacteria Clostridium pasteurianum, Desulfatomaculum reducens and Chlorobium limicola were placed far apart. Besides this, NifH amino acid sequences of Azospirillum brasilense showed quite divergence from the sequences observed in Paenibacillus durus and Roseiflexus castenholzii. Thus, NifH amino acid sequences classified various nitrogen-fixing bacteria into different phylogenetic clusters.

NifH protein, Amino acids, Phylogenetic analysis, Azospirillum, Nitrogen fixation, Bioinformatics

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Saurabh Sindhu, Divya Sindhu, S.K. Yadav, "Data Mining and Phylogenetic Analysis of NifH Protein of Azospirillum strain among Nitrogen-fixing Bacteria using Bioinformatics Tools," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.1-10, 2021.

This work presents real time drowsy driver detection and monitoring system using deep learning based behavioral approach. The aim is to design and implement software which captures live driver’s behavior during driving and train using convolutional neural network (CNN) to predict the behavior’s of the driver. This was achieved developing a drowsy driver dataset; intelligent video based device and the CNN architecture and configurations. The designs were implemented using deep learning tool and MATHLAB. The system was tested and the result showed a detection accuracy of 99.8%. MATHLAB was used to develop a prototype model of the system.

Drowsy behavior, Convolutional Neural Network, Training, Deep learning, MATHLAB

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P.E. Kekong, I.A. Ajah, U. Chidiebere, "Real Time Drowsy Driver Monitoring and Detection System Using Deep Learning Based Behavioural Approach," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.11-21, 2021.

Skin malignancy - an anomalous development of skin cells - frequently creates on uncovered skin. This basic type of malignant growth ordinarily happens on the skin without exposure of sunlight. There are three primary sorts of skin malignancy - basal cell carcinoma, squamous cell carcinoma and melanoma. It can be minimized the risk of skin disease by restricting or forestalling ultraviolet (UV) radiation. Testing the skin for dubious changes can help identify skin malignancy at a beginning phase. Automatic detection of skin cancer is a very effective tool, especially in the absence of specialists. Image processing has been practiced in various fields over the past decades, allowing to improve the interpretation, representation, and processing information of an image. Here the proposed system is based on Grey Level Co-occurrence Matrices (GLCM) and Support Vector Machine (SVM). A GLCM is a histogram of co-occurring greyscale values at a given counterbalance over an image. SVM kernel method has been used to classify the skin lesion and identify the type of skin cancer. System achieved 96.36 % of accuracy with minimal false alarm rate.

Skin Cancer, GLCM, Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Segmentation, Support Vector Machine

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Deepmala Sen, R.K. Chidar, "Automatic Skin Cancer Detection using GLCM & Support Vector Machine in Digital Image Processing," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.22-26, 2021.

Financial fraud presents more and more threat that has serious consequences in the financial sector. As a result, financial institutions are forced to continually improve their fraud detection systems. In recent years, several studies have used machine learning and data mining techniques to provide solutions to this problem. In this paper, we propose a state of art on various fraud techniques, as well as detection and prevention techniques proposed in the literature such as classification, clustering, And regression. The aim of this study is to identify the techniques and methods that give the best results that have been perfected so far. Financial fraud presents more and more threat that has serious consequences in the financial sector. As a result, financial institutions are forced to continually improve their fraud detection systems. In recent years, several studies have used machine learning and data mining techniques to provide solutions to this problem. In this paper, we propose a state of art on various fraud techniques, as well as detection and prevention techniques proposed in the literature such as classification, clustering, and regression. The aim of this study is to identify the techniques and methods that give the best results that have been perfected so far.

Financial fraud, clustering, regression, machine learning

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Saleha Farheen, Monika Raghuwanshi, "Performance of Machine Learning Techniques in the Prevention of Financial Frauds," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.27-29, 2021.

In this paper mammogram classification is introduced. The system takes mammogram, pre-processes it by applying Adaptive Histogram Equalization. The enhanced image is segmented using K Means Clustering algorithm. Statistical features such as standard deviation and mean of a segmented mammogram are extracted. SVM takes these features as input. DCT is applied on the segmented mammogram, these extracted features are fed as input to FFBPNN. These classify the mammogram as normal or abnormal. The training time of both the classifiers are compared to know which classifier takes less training time. The accuracy of the classifiers are determined by analyzing the results.

Mammograms, pre-process, SVM, FFBPNN

[1] S. Shanthi, V. Murali Bhaskaran, “Computer aided detection and classification of mammogram using self-adaptive resource allocation network classifier”, Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering , March 21-23, 2012
[2] Vijaylaxmi Kochari “Feed Forward Back Propagation Neural Network for Detection of Breast Cancer” International Journal of Computer Science Trends and Technology (IJCST) – Volume 6 Issue 6, Nov-Dec 2018, ISSN : 2347-8578.
[3] K Menaka, S Karpagavalli , “Mammogram Classification using Extreme Learning Machine and Genetic Programming,” International Conference on Computer Communication and Informatics (ICCCI -2014), Jan. 03 – 05, 2014
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[5] S .Bhadra “Analysis of Tumor Detection Methods for Mammogram Images” International Journal Of Computer Science and Engineering Vol.- 7, Issue- 5, May 2019 E-ISSN : 2347-2693
[6] M Arfan Jaffar, Nawazish Naveed, Sultan Zia, Bilal Ahmed and Tae-Sun Choi, “DCT Features Based Malignancy and Abnormality Type Detection Method For Mammograms” International Journal of Innovative Computing, Information and Control Volume 7, No.9, September 2011.
[7] Leonardo de Oliveira Martins, Geraldo Braz Junior, Aristofanes Correa Silva,Anselmo Cardoso de Paiva, and Marcelo Gattass_, “Detection of Masses in Digital Mammograms using K-means and Support Vector Machine”, Electronic Letters on Computer Vision and Image Analysis 8(2):39-50, 2009

Vijaylaxmi K. Kochari, "Multi-Model Analysis of Mammograms," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.30-35, 2021.

A ?-graceful labelling of a graph G =(V,E) , if a function f:V(G)?{0,1,2,…,n-1} is an injective function and the induced function f^*:E(G)?N is defined by f^* (e=mn)=2{f(m)+f(n)};where ?mn?E(G), then the resultant edge labels are distinct[3]. Here we discuss about some basic graph like as splitting graph of bistarB_(n,m),degree splitting graph of bistarB_(n,m),shadow graph of bistarB_(n,m),restricted square graph of bistar B_(n,m), barycentric sub division of bistarB_(n,m)and corona product ofbistarB_(n,m)with K1 admits?- graceful labelling.

Graceful labeling, Bistar Graph, Injective function

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Mehul Chaurasiya, Mehul Rupani, "?-Graceful Labeling In the Perspective of Graph Operations on Bistar," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.36-39, 2021.

Wireless sensor network is a type of Ad hoc network but routing protocols for WSN are different from MANET because the factors like power, communication type, node deployment etc. are different in both. WSN protocols are classified on different parameters like Network Structure and Protocol Operation. On the basis of network structure there are three types of routing protocol flat routing, hierarchical routing, and location based routing. Flat routing protocols uses flooding and hierarchical routing protocol worked base on clusters resulting hierarchical protocols are energy efficient and easily scalable. The main aim of this paper is to evaluate, analyze and compare four existing hierarchical based routing protocols named Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient Sensor Network (TEEN), Adaptive Threshold Sensitive Energy Efficient Sensor Network (APTEEN), and Distance Adaptive Threshold Sensitive Energy Efficient Sensor Network (DAPTEEN).

WSN, MANET, LEACH, TEEN, APTEEN, DAPTEEN

[1]. Verdone, R.; Dardari, D.; Mazzini, G.; Conti, A., “Wireless Sensor and Actuator Networks,” Elsevier: London, UK, 2008.
[2]. Manjeshwar A, Grawal D.P. TEEN: A protocol for enhanced efficiency in wireless sensor networks[C].Proceeding of the 15th Parallel and Distributed Processing Symp. San francisoi, 2001:2009-2015.
[3]. R.Devika, B.Santhi, T.Sivasubramanian “Survey on Routing Protocol in Wireless Sensor Network” ISSN: 0975-4024 Vol 5 No 1 Feb- Mar 2013
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[5]. Wendi Rabiner Heinzelman , Anantha Chandrakasan , Hari Balakrislman, "Energy-efficient communication protocol for wireless microsensor networks" , Jan 2000
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[7]. DaWei Xu, Jing Gao, “Comparison Study to Hierarchical Routing Protocols in Wireless Sensor Networks,” Procedia Environmental Sciences, Volume 10, Part A, 2011, Pages 595-600, ISSN 1878-0296
[8]. Manjeshwar, Arati & Agrawal, Dharma. APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks, 2002.
[9]. Anjali, Gaikwad et al. “Distance Adaptive Threshold Sensitive Energy Efficient Sensor Network (DAPTEEN) protocol in WSN.” 2015 International Conference on Signal Processing, Computing and Control (ISPCC): 114-119, 2015

Apurba Sinha, Smita Kishore, Pramod Kumar, "Comparative Study of Existing Hierarchical Based Routing Protocols for WSN," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.40-43, 2021.

The basic Fundamental to human-centric computer vision is to make the human motion see and understandable by machines. The hectic task is that the video containing enormous amount of information in the form of pixels, much of meaningless to a computer unless it can decode the data within the pixels. To make it possible, computer what is the mechanism behind which pixel go together and what it represents. The process of detecting and tracking the pixels representing the form of humans is to be notified as Human motion capture. Where there is a lacking of count of the people and we want to overcome. We plan to achieve this goal using intermediate level deep learning project on computer vision concepts, where deep learning is an AI method that imitate the functioning of human brain in processing data for use of object detection, speech recognition, translating languages, and making decisions. OpenCV is the place where it deals will all sorts of camera related things and make the detection easier. This work represents that how a human is detected and counted using SVM. The main idea is to detect the patterns of human motion, to a larger extent which is independent of differences in appearance. To do so, an HOG descriptor is used to detect the patterns of the frame captured, the greatest use of this descriptor is that it detects the patterns with the direction of the movement of the captured picture and hence it makes the job easy to train the pictures using the SVM and get the human detected.

Computer Vision; OpenCV; Support Vector Machine; HOG descriptor; Video Surveillance: Human Detection

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[5]. Sulman, N., Sanocki, T., Goldgof, D., & Kasturi, R., How effective is human video surveillance performance? In 2008 19th International Conference on Pattern Recognition, IEEE, pp. 1-3, December, 2008.
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(PDF) Real-Time Human Motion Detection and Tracking. Available from: https://www.researchgate.net/publication/251852856_Real-Time_Human_Motion_Detection_and_Tracking
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Chalavadi Sravanth, Gadde Harshavardhan, Kamineni. Kavya, Shaik Mohammad Akbar, Ch.M.H. Sai Baba, "Real-Time Human Detection in Video Surveillance," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.44-50, 2021.

This paper proposes an integration framework for the Public Institutions / government entities wishing to integration fragmented applications and systems. The existing alternatives of integration falls short of attributes like performance, interoperability, and reliability. This paper proposed a Microservices Based Integration and API driven approach that will achieve the resiliance and scale. We propose a model based on API and microservices framework leveraging API Gateway, API Management, ESB, Orchestrator, and Rules engine. This paper intends to provide a comprehensive list of integration capabilities based on the underlying integration framework. This framework is an enabler to digitally transform in government enterprise and to provide sufficient impetus and acceleration for its digital future.

e-Government, Service Orientation, API Management, Government Service Bus, Web Services, Integration Framework, Integraiton Platform

[1]. Suhail Madoukh, Rebhi Baraka, “A SOA-Based e-Government Data Integration”, International Arab Journal of e-Technology, Vol.3, Issue.3, pp.138-145, 2014.
[2]. Abswaidi Ramadhani, Anael Sam, Khamisi Kalegele, “Framework for integrating fragmented information systems: Case of livestock information systems”, International Journal of Computer Engineering Research, Vol.7, Issue.1, pp.1-10, 2017.
[3]. Abdulelah Awadh Al-Rashedi, “E-Government Based on Cloud Computing and Service-Oriented Architecture”, International Journal of Computer and Electrical Engineering, Vol.6, Issue.3, 2014.
[4]. Vojkan Nikoli?, Jelica Proti?, Predrag ?ikanovi?, “eGovernment interoperability in the context of European Interoperability Framework (EIF)”, ICIST, 2014.
[5]. Anu Paul, Varghese Paul, “A Framework for e-Government Interoperability in Indian Perspective”, International Journal of Computer Information Systems and Industrial Management Applications, Volume.6, pp.582-591, 2014.
[6]. Rashed Salem, Samah Abd Elrahman, Mohammed Hassan, “A Cloud-Based Data Integration Framework for eGovernmentService”, Journal of Computers, Vol.13, Issue.8, pp.913-923, 2018.
[7]. Abdullah AlHajri, Zuhoor Al-Khanjari, Naoufel Kraiem, and Yessine Al Jamoussi, “Enhanced e-Government Integration Framework for Higher Interoperability in e-Government Initiatives” IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES, India, 2017.
[8]. Iulian ILIE-NEMEDI, “Advanced eGovernment Information Service Bus (eGov-Bus)”, Informatica Economic?, Vol.0, Issue.1, pages 67-73, 2007.
[9]. Masethe HD, Olugbara OO, Ojo SO and Adewumi AO, “Integration of Health Data using Enterprise Service Bus”, Proceedings of the World Congress on Engineering and Computer Science, USA, 2013.

Sameer S. Paradkar, "eGovernment Integration Framework for Fragmented Systems," International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.51-55, 2021.