An effective face recognition system based on Cloud based IoT with a deep learning model
Cloud; Deep learning; Deep neural network; Edge computing; IoT
As of late, the Internet of Things (IoT) innovation has been utilized in applications, for example, transportation, medical care, video observation, and so on. The quick appropriation and development of IoT in these segments are producing an enormous measure of information. For instance, IoT gadgets, for example, cameras produce various pictures when utilized in medical clinic reconnaissance sees. Here, face acknowledgement is one of the most significant instruments that can be utilized for clinic affirmations, enthusiastic discovery, and identification of patients, location of fake gadgets. patient, and test clinic models. Programmed and shrewd face acknowledgement frameworks are profoundly precise in an overseen climate; notwithstanding, they are less exact in an unmanaged climate. Additionally, frameworks must keep on running on numerous occasions in different applications, for example, insightful wellbeing. This work presents a tree-based profound framework for programmed face acknowledgement in a cloud climate. The inside and out pattern have been proposed to cost less for the PC without focusing on unwavering quality. In the model, the additional size is isolated into a few sections, and a stick is made for each part. The tree is characterized by its branch area and stature. The branches are spoken to by a leftover capacity, which comprises of a twofold layer, a stack game plan, and a non-direct capacity. The proposed technique is assessed in an assortment of generally accessible information bases. An examination of the method is likewise finished with top to bottom craftsmanship models for the eye to eye connection. The aftereffects of the tests indicated that the example was considered to have accomplished a precision of 98.65%, 99.19%, and 95.84%. 2020
Chauhan D.; Kumar A.; Bedi P.; Athavale V.A.; Veeraiah D.; Pratap B.R.
Microprocessors and Microsystems, Vol-81
https://doi.org/10.1016/j.micpro.2020.103726 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098459051&doi=10.1016%2fj.micpro.2020.103726&partnerID=40&md5=6b75cb583d66dd2a3b45ac8179d0ee80
Chauhan D., Department of Computer Science & Engineering, Shivajirao Kadam Institute of Technology & Management, Indore, Madhya Pradesh, India; Kumar A., Assistant Professor &Director (Hon.), P.G. Department Of Law, Centre for Cyber Law and Policy Research, SRNCT, Faculty of Legal Studies, M.J.P. Rohilkhand University, Bareilly, Uttar Pradesh, India; Bedi P., Associate Professor, Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India; Athavale V.A., Department of Computer Science and Engineering, Panipat Institute of Engineering & Technology, Panipat, Haryana, India; Veeraiah D., Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering (A), Mylavaram, Krishna Dt., Andhra Pradesh, India; Pratap B.R., Department of Computer Science & Engineering, CHRIST (Deemed to be University), Bengaluru, Karnataka, India
An Effective IoT based Vein Recognition Using Convolutional Neural Networks and Soft Computing Techniques for Dorsal Vein Pattern Analysis
AI; BOSPHORUS; EER; KNN; ROI; SVM; VGG Net-16
In this research, we provide a CNN-based system that can reliably identify the dorsal veins of the hand. In order to get better results on different picture quality datasets, the suggested model makes use of refined variants of the pre-trained VGG Net-16 and VGG Net-19 designs. We use the BOSPHORUS dataset, which provides medium-quality photos, in addition to two self-constructed datasets that provide good-and low-quality images. By using state-of-the-art augmenting image methods, streamlined pre-processing procedures, and meticulously designed CNN designs, the fine-tuned VGG Net-16 model achieves superior performance in comparison to all other models. Using ROI pictures with a resolution of 22424 pixels, a multi-class technique is employed for arranging the vein patterns. Improving data quality during training makes the approach more broad, which helps prevent over fitting. On every dataset, the proposed method achieves better results than standard ML models like K-NN and SVM, and the experimental outcomes demonstrate significant improvements in accuracy. The modifying process led to a considerable decrease in the equal error rates (EER) when compared to benchmark methods. The structure enhances efficiency in computing with GPU-accelerated studying. It was built with the help of Python extensions like as OpenCV, Keras, and TensorFlow. Results from extensive testing of the proposed method show an accuracy of 99.98%, a precision of 98.98%, and a recall of 98.8%. From what we can see, the technique is both adaptable and dependable; making it well suited for use in practical biometrics vein recognition applications. 2025, American Scientific Publishing Group (ASPG). All rights reserved.
Bhimaavarapu, Krishna; Devi, Bylapudi Rama; Mahato, Chandra Bhushan; Kasireddy, Lakshmi Chandrakanth; Vadivukarassi, M.; Sivaraman, P.
Journal of Intelligent Systems and Internet of Things;Volume;16;Issue;2;pp.26-41
American Scientific Publishing Group (ASPG)
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.54216/JISIoT.160203 https://www.scopus.com/pages/publications/105001641835?origin=resultslist
Bhimaavarapu K., Dept. Of CSE, Koneru Lakshmaiah Education Foundation, (Deemed to be University), Vaddeswaram, Guntur, A.P, India; Devi B.R., Dept. Of ECE, Anil Neerukonda Institute of Technology & Sciences, AP, Visakhapatnam, India; Mahato C.B., MIT Muzaffarpur, Bihar, India; Kasireddy L.C., R&D-Engineering, ThoughtSpot Inc, Franklin, TN, United States; Vadivukarassi M., Department of CSE, St. Martin's Engineering College, Telangana, Secunderabad, India; Sivaraman P., Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, TN, Erode, India
An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown
COVID-19; epidemiology; mathematical model; ordinary differential equations; SEIHRD
There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemics evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to Indias diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that Indias two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second waves severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Harjule P.; Poonia R.C.; Agrawal B.; Saudagar A.K.J.; Altameem A.; Alkhathami M.; Khan M.B.; Hasanat M.H.A.; Malik K.M.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/healthcare10050759 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129155096&doi=10.3390%2fhealthcare10050759&partnerID=40&md5=810ac6d79a12a8bd8e9fc17f2eaaccb6
Harjule P., Department of Mathematics, Malaviya National Institute of Technology (MNIT), Jaipur, 302017, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Agrawal B., Department of Computer Science Engineering, Indian Institute of Information Technology Kota, MNIT Campus, Jaipur, 302017, India; Saudagar A.K.J., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Altameem A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Alkhathami M., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Khan M.B., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Hasanat M.H.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Malik K.M., Department of Computer Science and Engineering, Oakland University, Rochester, 48309, MI, United States
An Effective Time Series Analysis for Equity Market Prediction Using Deep Learning Model
Deep Learning; Long Short Term Memory; Recurrent Neural Network; Stock Price Prediction
A stock Exchange is a market where securities are traded. Every day, billions are traded at various stock exchanges across the world. In recent years prediction of movement of stock market is regarded as fascinating and has created a demand in financial market time series prediction. A precise forecasting of equity market is needed to provide higher returns for investors. Since there is high complexity in predicting stock market profits, developing models for it becomes difficult. The data mining and machine learning techniques has played an important role in Prediction of stock market movement. This study attempted to develop a deep learning model using Recurrent Neural Network for forecasting movement in the National Stock Exchange of India's benchmark broad based stock market index(NIFTY 50) for the Indian equity market. In this paper the NIFTY 50 index and INFYOSYS Ltd historical data from Yahoo finance companies has been selected for forecasting and analysis. 2019 IEEE.
Sachdeva A.; Jethwani G.; Manjunath C.; Balamurugan M.; Krishna A.V.N.
2019 International Conference on Data Science and Communication, IconDSC 2019
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/IconDSC.2019.8817035 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072782238&doi=10.1109%2fIconDSC.2019.8817035&partnerID=40&md5=2587cbd9a6758a43bfb8894496348c8a
Sachdeva A., Department of Computer Science and Engineering, Christ, Deemed to be University, Bangalore, India; Jethwani G., Department of Computer Science and Engineering, Christ, Deemed to be University, Bangalore, India; Manjunath C., Department of Computer Science and Engineering, Christ, Deemed to be University, Bangalore, India; Balamurugan M., Department of Computer Science and Engineering, Christ, Deemed to be University, Bangalore, India; Krishna A.V.N., Department of Computer Science and Engineering, Christ, Deemed to be University, Bangalore, India
An effectuation perspective on the development of global e-commerce among small and medium-sized /
Patent Number: 202211041077, Applicant: Amrita Chaurasia. E-commerce that spans international borders makes it possible for smaller businesses to more swiftly penetrate many international marketplaces. The purpose of this article is to investigate the ways in which effective market creation influences the international performance of small and medium-sized businesses (SMEs) that are involved in international e-commerce. Using the effectuation theory as a foundation, we propose that businesses can generate demand in foreign markets by developing innovative new ways for customers to interact and engage with them in the digital world.
https://drive.google.com/file/d/1N9o9iePfCzoT9GrbcgDNt4SuVQn7VawT/view
An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis
Deep Learning; Detection Cyberbullying; DistilBERT; Natural Language Processing; Sentiment Analysis
Particularly because of the complex and changing character of online communication, which hampers conventional detection strategies, the frequency of cyberbullying presents a significant threat to mental health and well-being in the digital age. This article presents a fast deep learning approach to improve cyberbullying detection by combining sentiment analysis with a lightweight transformer model, DistilBERT. This work intends to increase classification performance by using sentiment-based features and using DistilBERT's language and contextual awareness. Unlike conventional approaches and simpler machine learning methods, which can depend on feature extraction techniques like Bag of Words (BOW) or TF-IDF, the proposed model directly leverages contextual embeddings. Moreover, DistilBERT provides a balance between speed and performance unlike deep learning models like CNN, BLSTM, and LSTM, which could suffer with computational efficiency. Experimental results demonstrating remarkable accuracy and recall on many different datasets indicate the effectiveness of our hybrid approach. demonstrating a significant rise in cyberbullying detection over conventional methods, to evaluate performance criteria including computational efficiency, accuracy, and F1-score. With an outstanding 93.7 % accuracy, the proposed model exceeded earlier evaluated methods on this dataset. 2025 IEEE.
Kumar, Ravinder; Sharma, Dimpal; Kumar, Ajay; Hemrajani, Naveen; Poonia, Ramesh Chandra
2025 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2025 - Proceedings;pp.185-190
https://doi.org/10.1109/ETNCC66224.2025.11299715 https://www.scopus.com/pages/publications/105031883634?origin=resultslist
Kumar R., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Sharma D., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Kumar A., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Hemrajani N., Jecrc University, Department of Computer Science & Engineering, Jaipur, India; Poonia R.C., Department of Computer Science, Christ University, Bangalore, India
An efficient 2-Step DNA symmetric cryptography algorithm based on dynamic data structures
Bio-computing; Bio-Encryption; Cryptography; DNA; DNA Cryptography; Security
The security of text has become highly demanding in today's fast growing networking world. DNA computing is one of the emerging technologies in the arena of huge data storage and parallel computation. A single gram of DNA holds 5.5 petabytes of data. This leads to the increased risk in data communication. DNA in computers is mapped to human genome. Thus, the sequence of nucleotide base constructs the foundation of uniqueness. In this paper, a new scheme acronymed as -'Cryptography on DNA Storage'-CDS is provided. It performs the DNA data encryption in just two-step by using random private key for each letter in the plaintext and parallel swapping of the resultant text in small clusters. It is discussed keeping the time and space complexity of the algorithm in concern. 2018 Authors.
International Journal of Engineering and Technology(UAE), Vol-7, No. 2, pp. 141-146.
https://doi.org/10.14419/ijet.v7i2.6.10140 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044073105&doi=10.14419%2fijet.v7i2.6.10140&partnerID=40&md5=20ba61ca2c85c96749122aeb26a54ce1
George J.P., Christ Institute of Management, Lavasa, Pune, India; Kureethara J.V., CHRIST (Deemed to be University), India
AN EFFICIENT ACCESS POLICY WITH MULTI-LINEAR SECRET-SHARING SCHEME IN CIPHERTEXT-POLICY ATTRIBUTE-BASED ENCRYPTION
Access Policy; Ciphertext Policy Attribute Based Encryption; Encryption; Multi-Linear Secret-Sharing Scheme
Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a system in which attribute are used for user's identity and data owner determine the access policy to the data to be encrypt. Here access policy are attached with the ciphertext. In the form of a monotone Boolean formula monotone access structure, an access policy can be interpreted and a linear secret-sharing scheme (LSSS) can be implemented. In recent CP-ABE schemes, LSSS is a matrix whose row represent attributes and there exist a general algorithm which is proposed by Lewko and Waters it transforms a Boolean formula into corresponding LSSS matrix. But we may want to transform the monotone Boolean formula to an analogous but compressed formula first before applying the algorithm. This is a very complex procedure and require efficient optimization algorithm for obtaining equivalent but smaller size Boolean formula. So in this paper we are introducing an extended LSSS called multi-linear secret-sharing scheme where we can eliminate above optimization algorithm and directly convert any Boolean formula to multi-linear secret-sharing scheme. 2022 Little Lion Scientific. All rights reserved.
Ancy P.R.; Krishna A.V.N.; Balachandran K.; Balamurugan M.
Journal of Theoretical and Applied Information Technology, Vol-100, No. 5, pp. 1404-1411.
Ancy P.R., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishna A.V.N., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Balachandran K., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Balamurugan M., CSE Dept, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips
Dynamic XY routing algorithm; Network on chip; Random traffic profile; System on chip; Transpose traffic profile
The deadlock-free and live lock-free routing at the same time is minimized in the network on chip (NoC) using the proposed adoptive reconfigurable routing protocol (ARRP). Congestion condition emergencies are avoided using the proposed algorithm. The input packet distribution process is improved among all its shortest paths of output points. The performance analysis has been initiated by considering different configuration (N*N) mesh networks, by sending various ranges of data packets to the network on chip. The average and maximum power dissipation of XY, odd-even, Dy-XY algorithm, and proposed algorithm are determined. In this paper, an analysis of gate utilization during data packet transfer in various mesh configurations is carried out. The number of cycles required for each message injection in different mesh configurations is analyzed. The proposed routing algorithm is implemented and compared with conventional algorithms. The simulation has been carried out using reconfigurable two-dimensional mesh for the NoC. The proposed algorithm has been implemented considering array size, the routing operating frequency, link width length, value of probability, and traffic types. The proposed ARRP algorithm reduces the average latency, avoids routing congestion, and is more feasible for NoC compared to conventional methods. 2024 Institute of Advanced Engineering and Science. All rights reserved.
Sukumar P.G.; Krishnaiah M.; Velluri R.; Satish P.; Nagaraju S.; Puttaswamy N.G.; Srikantaswamy M.
International Journal of Electrical and Computer Engineering, Vol-14, No. 1, pp. 305-314.
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijece.v14i1.pp305-314 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183651138&doi=10.11591%2fijece.v14i1.pp305-314&partnerID=40&md5=6a2df4965e88480bb8e309e96cce4627
Sukumar P.G., Department of Computer Science and Engineering, JSS Academy of Technical Education, Bangalore, India; Krishnaiah M., Department of Electronics and Communications, Vidya Jyothi Institute of Technology, Hyderabad, India; Velluri R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Satish P., Department of Electronics and Communications, K.S. Institute of Technology, Bangalore, India; Nagaraju S., Department of Electrical and Electronics Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India; Puttaswamy N.G., East Point College of Engineering and Technology, Bangalore, India; Srikantaswamy M., Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bangalore, India
An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
Explainable Artificial Intelligence; IoT; Security; Smart Healthcare
The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patients health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats. 2023 IEEE.
Seetharaman T.; Sharma V.; Balamurugan B.; Grover V.; Agnihotri A.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1066-1071.
https://doi.org/10.1109/SmartTechCon57526.2023.10391664 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184799769&doi=10.1109%2fSmartTechCon57526.2023.10391664&partnerID=40&md5=c133e7ba5cc502b676dd4c3bda212643
Seetharaman T., Department of CSE, CMR University, Bangalore, India; Sharma V., Computer Science Department, CHRIST (Deemed to be University), India; Balamurugan B., Shiva Nadar University, India; Grover V., School of Management, Noida Institute of Engineering and Technology, India; Agnihotri A., School of Business, Galgotias University, India
An Efficient andOptimized Convolution Neural Network forBrain Tumour Detection
Acceleration; Brain Tumour; CNN; Compression
Brain tumour is a life threatening disease and can affect children and adults. This study focuses on classifying MRI scan images of brain into one of 4 classes namely: glioma tumour, meningioma tumour, pituitary tumour and normal brain. Person affected with brain tumours will need treatments such as surgery, radiation therapy or chemotherapy. Pretrained Convolution Neural Networks such as VGG19, MobileNet, and AlexNet which have been widely used for image classification using transfer learning. However due to huge storage space requirements these are not effectively deployed on edge devices for creation of robotic devices. Hence a compressed version of these models have been created using Genetic Algorithm algorithm which occupies nearly 3040% of space and also a reduced inference time which is less by around 50% of original model. The accuracy provided by VGG19, AlexNet, MobileNet and Proposed CNN before compression was 92.18%, 89.45%, 93.75% and 96.85% respectively. Similarly the accuracy after compression for VGG19, AlexNet, MobileNet and Proposed CNN was 91.34%, 88.92%, 94.40% and 95.29%. 2023, Springer Nature Switzerland AG.
Agarwal M.; Sharma L.K.; Gupta S.K.; Garg D.; Jindal M.
Communications in Computer and Information Science, Vol-1781 CCIS, pp. 459-474.
Springer Science and Business Media Deutschland GmbH
Agarwal M., Bennett University, Greater Noida, 201310, India; Sharma L.K., Bennett University, Greater Noida, 201310, India; Gupta S.K., Bennett University, Greater Noida, 201310, India; Garg D., Bennett University, Greater Noida, 201310, India; Jindal M., Christ Deemed to be University Delhi NCR, Mariam Nagar, Ghaziabad, 201003, India
An efficient approach for fractional nonlinear chaotic model with Mittag-Leffler law
AB derivative; Chaotic system; Fixed point theorem; Homotopy analysis method; Laplace transform
In this work, we exemplify the behaviour of the nonlinear model of arbitrary order differential equations by adopting q-homotopy analysis transform method (q-HATM). In the present study, the illustrated scheme is a graceful amalgamation of Laplace transform with q-homotopy analysis algorithm and we considered arbitrary order derivative using Atangana-Baleanu (AB) operator. The suggested nonlinear system exhibits chaotic behaviour in nature with respect to considered initial conditions. Fixed point hypothesis heard present the existence and uniqueness for the attained solution. We exemplified suggested arbitrary order system with to illustrate and confirm the efficiency of the projected solution procedure. Further, the numerical simulation is illustrated and also the chaotic behaviour of the obtained result captured with respect to arbitrary order in terms of plots. The obtained results confirm the projected scheme is highly methodical, easy to implement and very powerful to exemplify the nature of the dynamical system of arbitrary order. 2021 The Author(s)
Veeresha P.; Prakasha D.G.; Abdel-Aty A.-H.; Singh H.; Mahmoud E.E.; Kumar S.
Journal of King Saud University - Science, Vol-33, No. 2
https://doi.org/10.1016/j.jksus.2021.101347 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100250207&doi=10.1016%2fj.jksus.2021.101347&partnerID=40&md5=98c594b7db3a907de0f06626a5343891
Veeresha P., Department of Mathematics, CHRIST University, Bengaluru, 560029, India; Prakasha D.G., Department of Mathematics, Faculty of Science, Davangere University, Shivagangothri, Davangere, 577007, Karnataka, India; Abdel-Aty A.-H., Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha, 61922, Saudi Arabia, Physics Department, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt; Singh H., Department of Mathematics, Post-Graduate College Ghazipur, 233001, Uttar Pradesh, India; Mahmoud E.E., Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia, Department of Mathematics, Faculty of Science, Sohag University, Sohag, 82524, Egypt; Kumar S., Department of Mathematics, National Institute of Technology, Jamshedpur, 831014, Jharkhand, India
An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis
Bio-inspired algorithms; High-dimensional data; Machine learning; Microarray technology; Shapley value analysis
The fast development of microarray technology has significantly assisted in the use of gene expression analysis to forecast cancer subtypes. Analyzing high-dimensional microarray data is still challenging, as existing hybrid methods cannot find highly discriminative genes. This study aims to use Shapley value analysis and hybrid bio-inspired algorithms to develop a scalable, parallel gene selection technique to increase computing efficiency and classification accuracy in high-dimensional microarray data. This study used hybrid feature selection approaches inspired by bio-organisms to create a scalable parallel gene selection system. The dataset size is initially enlarged by Adaptive Synthetic Sampling (ADASYN). They use the Recursive Feature Elimination (RFE) approach to extract features and determine their Shapley values. In addition, the Whale Optimization Algorithm (WOA) works to determine which genes are most important. After that, Machine Learning (ML) techniques assist in classifying the chosen characteristics. According to the experiment results, the suggested strategy surpasses standard gene selection techniques with the same datasets, employing improved classification accuracy and reducing computing time. K-NN achieved an accuracy of 85.44%, while LR showed improved results with an accuracy of 91.72%. RF further increased accuracy to 94.69%. SVM demonstrated exceptional performance, reaching an accuracy of 97.63%. Ultimately, XGBoost excelled among all models with the highest accuracy of 98.49%, highlighting its robust ability to classify SRBCT samples effectively based on gene expression data. 2025, Ayandegan Institute of Higher Education. All rights reserved.
Lakshmi Alluri, Vijaya; Pavan Kanadam, Karteeka; Josephine V L, Helen; Rajagopal, Manikandan
International Journal of Research in Industrial Engineering;Volume;14;Issue;3;pp.508-527
https://doi.org/10.22105/riej.2025.508954.1550 https://www.scopus.com/pages/publications/105012623487?origin=resultslist
Lakshmi Alluri V., Department of Computer Science and Engineering, Acharya Nagarjuna University, Andhra Pradesh, Guntur, India; Pavan Kanadam K., Department of Computer Applications, R.V.R & J.C College of Engineering, Andhra Pradesh, Guntur, India; Josephine V L H., Department of Business Analytics, School of Business and Management, Christ University, Karnataka, Bangalore, India; Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, Christ University, Karnataka, Bengaluru, India
An Efficient Approach for Obstacle Avoidance and Navigation in Robots
Bayesian algorithms; DQN algorithm; dynamic policy; extended dynamic policy; physical environment; real-world transfer.; reinforcement learning; reward signal; robot obstacle avoidance; simulation; static algorithms
Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focusing on Bayesian, static, dynamic policy, Deep Q-Learning (DQN) and extended dynamic policy algorithms. In the context of robot obstacle avoidance, these algorithms enable an agent to interact with its physical environment, learns effective operating strategies, and optimize actions to maximize a reward signal. The environment typically consists of a physical space that the robot must navigate without encountering obstacles. The reward signal serves as an objective measure of the robot's performance towards accomplishing specific goals, such as reaching designated positions or completing tasks. Furthermore, successful obstacle avoidance strategies acquired in simulation environments can be seamlessly transferred to real-world scenarios. The promising results achieved thus far indicate the potential of reinforcement learning as a powerful tool for enhancing robot obstacle avoidance. This research concludes with insights into the future prospects of reward learning, high-lighting its ongoing importance in the development of intelligent robotics systems. The proposed algorithm DQN outperforms well among all the other algorithms with an accuracy of 81%, Through this research, we aim to provide valuable insights and directions for further advancements in the field of robot obstacle avoidance using reinforcement learning techniques. 2023 IEEE.
Phani Shanmukh M.; Natarajan B.; Kannan C.; Tamilselvi M.; Vigneshwaran T.; Husain S.S.
International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023
Phani Shanmukh M., Amrita Vishwa Vidyapeetham, Amrita School of Computing, Department of Computer Science and Engineering (AIE), Chennai, India; Natarajan B., Vit University, School of Computer Science and Engineering, Chennai, India; Kannan C., Cmr Institute of Technology, Department of Computer Science and Engineering, Hyderabad, India; Tamilselvi M., Roever Engineering, Department of Computer Science and Engineering, Perambalur, India; Vigneshwaran T., Christ (Deemed to Be University), School of Engineering and Technology, Department of Cse, Bangalore, India; Husain S.S., K Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, Tiruchirapalli, India
An efficient approach towards clustering using K-means algorithm
Distance measure; Elbow method; Gap-statistic method; Hopkins index; Partition clustering; Standard K-means process
Cluster analysis is one of the major knowledge mining methods in the field of data analytics; the approach used for clustering will influence the accuracy of the results and quality of the obtained clusters. A good clustering process or algorithm is one which increases the fit of the data points in each cluster and which satisfies the clustering criteria, if these measures are not met adequately the desired pattern will not be seen and the patterns obtained for analysis may turn out to be inaccurate or insufficient. This paper discusses the standard k-means clustering algorithm and provides an efficient approach towards clustering using the standard global K-means algorithm; the process eliminates the need for initializing random number of clusters multiple times which is followed as the standard process in the field. The effectiveness of the proposed approach was analyzed using the benchmark dataset and the implementation was performed using the well-known analytic tool R Studio and supporting packages. IAEME Publication.
International Journal of Civil Engineering and Technology, Vol-9, No. 2, pp. 705-714.
Aiyappa S.N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network
Adaptive emperor penguin optimization; Deep learning; Imaging modalities; Liver cirrhosis
Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour. 2022 Elsevier Ltd
Shaheen H.; Ravikumar K.; Lakshmipathi Anantha N.; Uma Shankar Kumar A.; Jayapandian N.; Kirubakaran S.
Biomedical Signal Processing and Control, Vol-80
https://doi.org/10.1016/j.bspc.2022.104152 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140137549&doi=10.1016%2fj.bspc.2022.104152&partnerID=40&md5=e2e8f46a450dfafb8f36b3f824c8e37f
Shaheen H., Department of Computer Science and Engineering, MVJ College of Engineering, Bangalore, India; Ravikumar K., Dept. Of Cse, Rrase College Of Engg, Chennai Padappai, India; Lakshmipathi Anantha N., CSE Department, Malla Reddy Engineering College, Hyderabad, India; Uma Shankar Kumar A., K.S.R.M. College of Engineering, Andhra Pradesh, Kadapa, India; Jayapandian N., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Kirubakaran S., Department of Computer Science and Engineering, CMR College of Engineering and Technology, Hyderabad, India
An efficient cloud based architecture for integrating content management systems
Cloud; CMS; MYSQL; Open Source; PhP; REST; Web Services
The use of digital content is increasing day after day and now it is an essential element of our day today life. The amount of stored information is so huge that it is highly difficult to manage the content especially in a distributed cloud environment. There are many open source software solutions available in cloud to handle huge amount of digital data. However none of these solutions addresses all the requirements needed to manage the content spread out in multiple systems effectively. The user has to relay on multiple content management systems to do the work. This turns into ever more unwieldy, time consuming and leads to loss of data. Using robust and integrated content management systems, these issues could be solved effectively. In this paper we have identified various challenges of using the content management system in the cloud after surveying many Content Management System related article and proposed an integrated solution named Cloud based Architecture integrating Content Management System which is capable of interfacing with various unique features available at different content management system installations in the cloud. This maximizes the functionality and performance of any Content management systems. The Representational State Transfer (REST) protocol is used to integrate the best features of various open source content management systems. REST provides higher level of security compared to existing systems as it does not store the user sessions. The users can interact with the system with the help of an interface which abstracts the complexities of multiple content management systems running in the cloud. 2017 IEEE.
IEEE International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2017 - Proceedings, pp. 337-342.
https://doi.org/10.1109/ICIMIA.2017.7975631 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027452395&doi=10.1109%2fICIMIA.2017.7975631&partnerID=40&md5=7ba25a89a532b05824c7d2ca13c6badc
Thrivani J., University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Venugopal K.R., University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Thomas B., Department of Computer Science, Christ University, Bangalore, 560 029, India
An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks
Clustering; Mobile ad hoc network; Network lifetime; Route maintenance; Routing protocol; Star topology
Mobile ad hoc network (MANET) is arranged with multiple nodes that communicate wirelessly. However, MANET communication suffers from various issues such as inadequate security, low stability, high power consumption, and a lack of specific infrastructure of the network. Moreover, the route failure happened in the network due to the unrestricted node movement, which has increased energy utilization, delay, and reduced lifetime of the nodes. To overcome these issues, the novel Eagle Based Density Clustering (EBDC) approach is developed in this research that predicts the link failure and increased the lifetime of the nodes. Here, the developed EBDC approach is utilized for clustering and route maintenance in MANET for that it creates the nodes using the star topology. Initially, the developed approach selects the Cluster Head and transmits the message through the created path. Subsequently, the link failure is detected by the EBDC model, and it creates a new reference layer to replace the exhausted layer. Hence, the developed EBDC model has enhanced the network lifetime and reduced energy utilization. Furthermore, this model is implemented using Network Simulator 2, and the parameters like accuracy, energy consumption, Packet Delivery Ratio, network lifetime, end-to-end delay, and throughput are calculated. Additionally, the attained outcomes are compared with prevailing methods for evaluating the efficiency of the developed approach. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Journal of Ambient Intelligence and Humanized Computing, Vol-14, No. 1, pp. 305-319.
Vatambeti R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Sanshi S., Department of Information Science and Engineering, BMS Institute of Technology & Management, Bangalore, India; Krishna D.P., Department of Computer Science and Engineering, Narayana Engineering College, Andhra Pradesh, Guduru, India
An Efficient Comparison on Machine Learning and Deep Neural Networks in Epileptic Seizure Prediction
Area under curve (AUC); BONN dataset; Deep neural networks (DNN); Electroencephalogram (EEG); Epilepsy
Electroencephalography signals have been widely used in cognitive neuroscience to identify the brains activity and behavior. These signals retrieved from the brain are most commonly used in detecting neurological disorders. Epilepsy is a neurological impairment in which the brains activity becomes abnormal, causing seizures or unusual behavior. Methods: The benchmark BONN dataset is used to compare and assess the models. The investigations were conducted using the traditional algorithms in machine learning algorithms such as KNN, naive Bayes, decision tree, random forest, and deep neural networks to exhibit the DNN models efficiency in epileptic seizure detection. Findings: Experiments and results prove that deep neural network model performs more than traditional machine learning algorithms, especially with the accuracy value of 97% and area under curve value of 0.994. Novelty: This research aims to focus on the efficiency of deep neural network techniques compared with traditional machine learning algorithms to make intelligent decisions by the clinicians to predict if the patient is affected by epileptic seizures or not. So, the focus of this paper helps the research community dive into the opportunities of innovations in deep neural networks. This research work compares the machine learning and deep neural network model, which supports the clinical practitioners in diagnosis and early treatment in epileptic seizure patients. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Roseline Mary R.; Zoraida B.S.E.; Ramamurthy B.
Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 677-687.
Roseline Mary R., Bharathidasan University, Tamil N?du, Tiruchirappalli, India, CHRIST (Deemed To Be University), Karnataka, Bangalore, India; Zoraida B.S.E., Bharathidasan University, Tamil N?du, Tiruchirappalli, India; Ramamurthy B., CHRIST (Deemed To Be University), Karnataka, Bangalore, India
An Efficient Compressive Data Collection Scheme for Wireless Sensor Networks
Compressive data collection; covariance function; Gaussian regression; margin-free estimator; maximum likelihood estimator; wireless sensor network
The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Mary Anita E.A.; Jenefa J.; Vinodha D.; Lapina M.
Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 31-47.
Mary Anita E.A., CHRIST (Deemed to be University), Bangalore, India; Jenefa J., CHRIST (Deemed to be University), Bangalore, India; Vinodha D., CHRIST (Deemed to be University), Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, Russian Federation