Enhanced Energy Efficient Routing for Wireless Sensor Network Using Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
E-PEGASIS; Energy efficient routing; LEACH; PEGASIS; Wireless Sensor Networks
Recent technological advancement in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmitted to the base station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can communicate with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this paper we have proposed Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy efficient data transmission based on PEGASIS protocol. In this proposed method average distance between the sensor nodes are considered as the criterion for chaining and fix the outermost node's radio range value the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission between the sensor node and the base station. The simulation of the proposed work shows that lifetime of the network is increased when comparing to the LEACH and PEGASIS protocol. 2021 The Authors. Published by Elsevier B.V.
Procedia Computer Science, Vol-194, pp. 89-101.
https://doi.org/10.1016/j.procs.2021.10.062 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121778596&doi=10.1016%2fj.procs.2021.10.062&partnerID=40&md5=f2da3288aea63661266b0c1720e426a7
Sadhana S., Department of Computer Science and Engineering, KalaignarKarunanidhi Institute of Technology, Coimbatore, India; Sivaraman E., Department of Computer Science and Engineering, PES University, Bangalore, India; Daniel D., Department of Computer Science and Engineering, Christ University, Bangalore, India
Enhanced Energy-Efficient Routing for Wireless Sensor Network Using Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
E-PEGASIS; Energy-efficient routing; LEACH; PEGASIS; Wireless sensor networks
Recent technological advancements in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmits to the Base Station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually, sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can transmit the data with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this manuscript, we have proposed Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy-efficient data transmission based on PEGASIS protocol. In this proposed method, the average distance between the sensor nodes is considered as the criterion for chaining and fixing the outermost nodes radio range value to the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission amid the base station and sensor node. The simulation of the proposed work shows that lifetime of the network is increased when compared to the LEACH and PEGASIS protocol. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Smart Innovation, Systems and Technologies, Vol-235, pp. 159-171.
Springer Science and Business Media Deutschland GmbH
Sadhana S., KalaignarKarunanidhi Institute of Technology, Coimbatore, India; Sivaraman E., PES University, Bangalore, India; Daniel D., Christ (Deemed To Be University), Bangalore, India
Enhanced Geographical Information System Architecture for Geospatial Data
Singh M.; Agarwal S.; Ajay Prasanna Y.; Jayapandian N.; Kanmani P.
New Trends in Computational Vision and Bio-Inspired Computing - Selected Works Presented at the ICCVBIC 2018, pp. 37-44.
Singh M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Agarwal S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Ajay Prasanna Y., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Kanmani P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India
Enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing Systems
Crowdsourcing; EHOA-IQOCSS; Location-based services; Query optimization
Crowdsourcing is a strategy of collecting information and knowledge from an abundant range of individuals over the Internet in order to solve cognitive or intelligence intensive challenges. Query optimization is the process of yielding an optimized query based upon the cost and latency for a given location based query. In this view, this article introduces an Enhanced Horse Optimization Algorithm based Intelligent Query Optimization in Crowdsourcing Systems (EHOA-IQOCSS) model. The presented EHOA-IQOCSS model mainly based on the enhanced version of HOA using chaotic concepts. The proposed model plans to accomplish a better trade-off between latency and cost in the query optimization process along with answer quality. The EHOA-IQOCSS is used to compute the Location-Based Services (LBS) namely K-Nearest Neighbor (KNN) and range queries, where the Space and Point of Interest (POI) can be obtained by the conviction level computation. The comparative study stated the betterment of the EHOA-IQOCSS model over recent methods. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Renukadevi M.; Anita E.A.M.; Geetha D.M.
Communications in Computer and Information Science, Vol-1797 CCIS, pp. 234-249.
Renukadevi M., Department of Electronics andCommunication Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, India; Anita E.A.M., Department of CSE, School of Engineering and Technology, CHRIST University, Bengaluru, India; Geetha D.M., Department of Electronics andCommunication Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, India
Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems
Computer-assisted diagnoses; Deep learning; Healthcare; Internet of Things; Jaya Optimization Algorithm; Medical imaging; Oral cancer
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches. 2024, American Scientific Publishing Group (ASPG). All rights reserved.
Rajkumar R.; Valluru D.; Siva Satya Sreedhar P.; Ramshankar N.; Sujatha S.; Somasundaram R.; Sudha M.; Navaneethan S.
Journal of Intelligent Systems and Internet of Things, Vol-11, No. 2, pp. 97-110.
American Scientific Publishing Group (ASPG)
https://doi.org/10.54216/JISIoT.110209 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189005327&doi=10.54216%2fJISIoT.110209&partnerID=40&md5=8e7251f6e1c2adb87150893aa151d51a
Rajkumar R., Department of ECE, Vel Tech Rangarajan Dr. Saguthala R&D institute of Science and Technology, Tamil Nadu, Chennai, India; Valluru D., Department of IT, MLRITM Engineering College, Hyderabad, India; Siva Satya Sreedhar P., Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Krishna district, Andhra Pradesh, Gudlavalleru, India; Ramshankar N., Department of Computer Science and Engineering, R. M. D. Engineering College, Kavaraipettai, Tamil Nadu, Tiruvallur, India; Sujatha S., Department of Electronics and communication Engineering, Christ University, School of Engineering and Technology, Bangalore, India; Somasundaram R., Management Studies Department, Kongu Engineering College, Erode, 638060, India; Sudha M., Department of ECE, Paavai Engineering College (Autonomous), Tamilnadu, Namakkal, India; Navaneethan S., Department of ECE, Saveetha Engineering College, Tamil Nadu, Chennai, India
Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
ANN; CNN; Machine Learning; Precision Agriculture; Smart Irrigation; SVM
This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Singh J.; Shelke N.A.; Hasan D.S.; Sajid M.; Alsahlanee A.T.R.; Upreti K.
Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 231-240.
Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Hasan D.S., Department of Computer Science and Information Technology, University of Salahaddin-Erbil, Erbil, Iraq; Sajid M., Department of Computer Science, Aligarh Muslim University, Aligarh, India; Alsahlanee A.T.R., Development and Continuous Education Center, University of Thi-Qar, Thi-Qar, Iraq; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India
Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
Brain Tumor; CNN; Deep Learning; MRI Classification; RESNET; VGG16
The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE.
Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
Institute of Electrical and Electronics Engineers Inc.
Sambandam R.K., Christ University, Dept. of Computer Science and Engineering, Bengaluru, India; Jyoshna M., Christ University, Dept. of Computer Science and Engineering, Bengaluru, India
Enhanced Light Scattering Using a Two-Dimensional Quasicrystal-Decorated 3D-Printed Nature-Inspired Bio-photonic Architecture
A number of strategies have been exploited so far to trap photons inside living cells to obtain high-contrast imaging. Also, launching light inside biological materials is technically challenging. Using photon confinement in a three-dimensional (3D)-printed biomimetic architecture in the presence of a localized surface plasmon resonance (LSPR) promoter can overcome some of these issues. This work compares optical confinement in natural and 3D-printed photonic architectures, namely, fish scale, in the presence of atomically thin Al70Co10Fe5Ni10Cu5 quasicrystals (QCs). Due to their wideband LSPR response, the QCs work as photon scattering hotspots. The architecture acts as an additive source of excitation for the two-dimensional (2D) QCs via total internal reflection (TIR). The computational analysis describes the surface plasmon-based scattering property of 2D QCs. The 3D-printed fish scale's image contrast with the 2D Al70Co10Fe5Ni10Cu5 QC has been compared with other 2D materials (graphene, h-BN, and MoS2) and outperforms them. The present study conceptually presents a new approach for obtaining high-quality imaging of biological imaging, even using high-energy photons. 2023 American Chemical Society.
Kumbhakar P.; Pramanik A.; Mishra S.S.; Tromer R.; Biswas K.; Dasgupta A.; Galvao D.S.; Tiwary C.S.
Journal of Physical Chemistry C, Vol-127, No. 20, pp. 9779-9786.
https://doi.org/10.1021/acs.jpcc.3c00513 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160735552&doi=10.1021%2facs.jpcc.3c00513&partnerID=40&md5=5733e0f1514538bf53a250f1496e0fac
Kumbhakar P., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India, Department of Physics and Electronics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Pramanik A., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; Mishra S.S., Department of Materials Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India; Tromer R., Applied Physics Department, University of Campinas, Campinas, 13083-872, Brazil; Biswas K., Department of Materials Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India; Dasgupta A., Physical Metallurgy Division, Materials Metallurgy Group, Indira Gandhi Center for Atomic Research, HBNI, Kalpakkam, 603102, India; Galvao D.S., Applied Physics Department, University of Campinas, Campinas, 13083-872, Brazil; Tiwary C.S., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods
Deep Learning; ECLAT Algorithm; Lesk Algorithm; Lumpy skin disease; Prediction
Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE.
Lamani M.R.; Padmaja K.; Shilpa B.L.; Asha V.
2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024
https://doi.org/10.1109/NMITCON62075.2024.10699233 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207421713&doi=10.1109%2fNMITCON62075.2024.10699233&partnerID=40&md5=47605afd71b949cca44a50a97f997dda
Lamani M.R., CHRIST(Deemed to be University), Dept of Cse, Bangalore, India; Padmaja K., Gsssietw, Dept of Ise, Mysore, India; Shilpa B.L., Atmece, Dept of Cse, Mysore, India; Asha V., Rns Institute of Technology, Dept of Cse, Bangalore, India
Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
3DCNN; Attention Mechanism; Deep Learning; IoT; MobilenetSSD; YOLOv8
For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE.
1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024 - Proceedings, pp. 355-360.
Agarwal V., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Singh M., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Prathap B.R., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India
Enhanced photocatalytic activity of La3+ doped bicrystalline titania prepared via combustion method for the degradation of cationic dye under solar illumination
Combustion synthesis; Degradation of cationic dye; La3+ doped titania; Photocatalysis; Titania
La3+ doped TiO2 photocatalysts were successfully synthesized by combustion method in the presence of urea and were characterized by various physico-chemical techniques. Further, the photocatalytic performance of the synthesized catalysts was monitored by photocatalytic degradation of synthetic cationic dye-Methylene Blue (MB) under solar illumination. The bicrystalline phase of anatase and rutile was confirmed by X-ray diffraction analysis. Moreover, the transformation from anatase to rutile phase proceeds at a slower rate in the La3+ doped TiO2 catalysts. Effective separation of charge carriers, a synergistic effect in the bicrystalline framework of anatase and rutile, smaller crystallite size, and higher concentration of surface adsorbed hydroxyl groups helped these catalysts to show improved activity for the dye degradation. Copyright 2018 BCREC Group. All rights reserved.
Bulletin of Chemical Reaction Engineering and Catalysis, Vol-13, No. 1, pp. 119-126.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.9767/bcrec.13.1.1427.119-126 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041186449&doi=10.9767%2fbcrec.13.1.1427.119-126&partnerID=40&md5=f2eaeec4287940c6ec361d25efceac7c
Nair R.R., Department of Chemistry, Christ University, Bangalore, 560029, Karnataka, India; Mohan M.K., Department of Sciences and Humanities, Christ University, Bangalore, 560074, Karnataka, India; Sunajadevi K.R., Department of Chemistry, Christ University, Bangalore, 560029, Karnataka, India
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Cavity Segmentation; Enhanced U-Net; Post-Operative Brain MRI; Skull Removal; VGG16 Backbone
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G.
Journal of Computer Science, Vol-20, No. 5, pp. 585-593.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.3844/JCSSP.2024.585.593 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194284446&doi=10.3844%2fJCSSP.2024.585.593&partnerID=40&md5=dd8a104fd6768b13e9b519e68bbde065
Sobha X.P., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Sathish P.K., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Raju G., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India
Enhanced power quality control of a photo voltaic power plant integrated with multiple electric vehicle
Distribution side generation (DSG); DQ control; electric vehicle; multicarrier SVPWM; photo-voltaic (PV)
As there is a great need for high-quality electricity on the distribution side, distribution side generation (DSG) has become increasingly important. The increased weight of EVs on the distribution side is the cause of this. There are numerous power quality mitigation techniques employed to address this type of issue, but many of the solutions suggest the usage of a separate device, such as an active power filter. But while construction the DSG the solution to this problem may be addressed using the proposed solution in this paper. Power quality (PQ) problems are being caused by the grids integration of Photo-Voltaic (PV) and its application to all connected loads. With the aid of Direct Quardrature (DQ) controller and Multicarrier Space Vector Pulse Width Modulation (SVPWM) technology, the overall power quality disturbance is decreased. A Simulink model for the PV-EV-Grid system was built to measure voltage and current Total Harmonic Distortion (THD) percentages under linear, non-linear, and Plug in Hybrid Vehicle (PHEV) load situations. The model shows that the THD values are well within the IEEE 519. Indian Academy of Sciences 2024.
Sadhana - Academy Proceedings in Engineering Sciences, Vol-49, No. 4
Harish B.N., Department of Electrical and Electronics Engineering, School Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Surendra U., Department of Electrical and Electronics Engineering, School Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
Enhanced Process Model and Analysis of Risk Integration in Software effort estimation
Multi-Layer Perceptron model; Process Model; Risk score; Risk-integrated Effort estimation
The development of software within the estimated effort is remaining as a challenging task. The process of effort estimation is a critical activity in a software project, where effort estimates are utilized to arrive at the schedule, resources, and cost. Though many software effort estimation techniques exist, effort overrun occurs in a project. Identification of risks and their consideration in software lifecycle activities play a significant role in the successful execution of a software project. It would be required to account for uncertainty and the key factors that contribute to it. This study focuses on the need to include project risk score in the software effort estimation process to arrive at better effort estimates. This paper depicts the standard and enhanced process frameworks for estimation of software development efforts. A multi-layer perceptron model was built and the results indicated the relevance of considering project risk score in the effort estimation process. The usage of an enhanced gradient boosting technique for predictive modelling revealed a decrease in standard deviation of the residuals, thus indicating a better fit for the effort estimation model through integration of risks. 2019 IEEE.
Ramakrishnan N.; Girijamma H.A.; Balachandran K.
Proceedings of the 2nd International Conference on Smart Systems and Inventive Technology, ICSSIT 2019, pp. 419-422.
https://doi.org/10.1109/ICSSIT46314.2019.8987841 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080052336&doi=10.1109%2fICSSIT46314.2019.8987841&partnerID=40&md5=88b4cf789002804c1549ddca2c1f1ccb
Ramakrishnan N., CHRIST Institute of Management, CHRIST (Deemed to Be University), Bengaluru, India; Girijamma H.A., RNS Institute of Technology, Computer Science and Engineering Department, Bengaluru, India; Balachandran K., CHRIST (Deemed to Be University), Computer Science and Engineering Department, Bengaluru, India
Enhanced radial basis function neural network for tomato plant disease leaf image segmentation
Enhanced radial basis function; Feature extraction; Modified sunflower optimization; Segmentation; Tomato leaf disease
Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V.
https://doi.org/10.1016/j.ecoinf.2022.101752 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135888868&doi=10.1016%2fj.ecoinf.2022.101752&partnerID=40&md5=fae568c73ea3f425f0a62d0a0e3c1e1b
Patil M.A., Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India, Assistant Professor, Department of Information Technology, Vasavi College of Engineering, Ibrahimbagh, Telangana, Hyderabad, 500031, India; M M., Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT
The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication. 2022 Anand Kumar et al.
Kumar A.; Rahmath M.; Raju Y.; Reddy Vulapula S.; Prathap B.R.; Hassan M.M.; Mohamed M.A.; Asakipaam S.A.
Security and Communication Networks, Vol-2022
Kumar A., Cambridge Institute of Technology, North Campus, Karnataka, Bangalore, 562110, India; Rahmath M., Department of Computer Science, Prince Sattam Bin Abdulaziz University, Wadi Ad-Dawasir, Saudi Arabia; Raju Y., Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Telangana, Hyderabad, India; Reddy Vulapula S., Department of IT, Vignana Bharathi Institute of Technology, Hyderabad, India; Prathap B.R., Department of Computer Science and Engineering, Christ University, Bangalore, India; Hassan M.M., Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Mohamed M.A., Department of Medical Instruments Engineering Techniques, AI-Turath University, Baghdad, 10021, Iraq, Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10021, Iraq; Asakipaam S.A., Department of Electrical and Electronics Engineering, Tamale Technical University, Tamale, Ghana
Enhanced Security for Large-Scale 6G Cloud Computing: A Novel Approach to Identity based Encryption Key Generation
Cloud Computing; Cloud Security; Equality Test; Identity-Based Encryption; Large Scale 6G
Cloud computing and 6G networks are in high demand at present due to their appealing features as well as the security of data stored in the cloud. There are various challenging methods that are computationally complicated that can be used in cloud security. Identity-based encryption (IBE) is the most widely used techniques for protecting data transmitted over the cloud. To prevent a malicious attack, it is an access policy that restricts access to legible data to only authorized users. The four stages of IBE are setup, key extraction or generation, decryption and encryption. Key generation is a necessary and time-consuming phase in the creation of a security key. The creation of uncrackable and non-derivable secure keys is a difficult computational and decisional task. In order to prevent user identities from being leaked, even if an opponent or attacker manages to encrypted material or to decode the key this study presents an advanced identity-based encryption technique with an equality test. The results of the experiments demonstrate that the proposed algorithm encrypts and decrypts data faster than the efficient selective-ID secure IBE strategy, a competitive approach. The proposed method's ability to conceal the identity of the user by utilizing the Lagrange coefficient, which is constituted of a polynomial interpolation function, is one of its most significant aspects. 2023 The Authors. Published by AnaPub Publications. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Rathinam G.; Balamurugan M.; Arulkumar V.; Kumaresan M.; Annamalai S.; Bhuvana J.
Journal of Machine and Computing, Vol-3, No. 2, pp. 80-91.
Rathinam G., Information and communication Engineering, College of Engineering, University of Buraimi, Al Buraimi, Oman; Balamurugan M., Department of CSE, CHRIST(Deemed to be University), Bengaluru, 560074, India; Arulkumar V., School of Computer Science and Engineering, Vellore Institute of Technology, TamilNadu, Vellore, India; Kumaresan M., Department of Computer Science and Engineering, Jain Deemed-to-be University, Karnataka, India; Annamalai S., Department of Computer Science and Engineering, Jain Deemed-to-be University, Karnataka, India; Bhuvana J., Department of CSIT, Jain(Deemed to be University), Bengaluru, 560069, India
Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
Facial Recognition (FR); Field Programmable Gate Arrays (FPGA); Payment System
Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE.
Gowsic K.; Sowmiya M.; Nirmala Devi M.; Chauhan A.; Arul Mary Rexy V.; Singh D.
1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
Gowsic K., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Sowmiya M., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Nirmala Devi M., School of Sciences, CHRIST (Deemed to be University), Department of Life Sciences, Karnataka, Bengaluru, India; Chauhan A., St. Martin's Engineering College, Department of English, Secunderabad, India; Arul Mary Rexy V., Saveetha College of Liberal Arts and Sciences, SIMATS, Department of Commerce, Kuthambakkam, India; Singh D., School of Management Sciences, Department of Applied Science, Lucknow, India
Enhanced Sensing Performance of an Ammonia Gas Sensor Based on Ag-Decorated ZnO Nanorods / Polyaniline Nanocomposite
Ammonia Gas Sensor; Nanocomposites; Polyaniline; Silver; Zinc Oxide
The development of low-cost ammonia sensors with high sensitivity and selectivity has gained considerable interest. Though the response of these sensors at room temperature is low and needs enhancement. In the present study high sensitivity ammonia gas sensors based on nanocomposite films of polyaniline (PANI) and with varying ZnO concentrations were synthesized and investigated. With a loading of 10 at% ZnO, the gas sensing response of 59 % was obtained for 120 ppm NH3 gas. The gas response was further enhanced by decorating the ZnO nanorods with different concentrations of silver (Ag) nanoparticles. The Ag-decorated ZnO nanorods were embedded in the PANi matrix using the in-situ oxidative polymerization technique. It was shown that PANi ZnO, p-n junction, and the introduction of porosity in nanocomposite act synergistically in increasing the resistance caused by the deprotonation of PANi by NH3. Among various compositions studied, 2 % loading of Ag in ZnO embedded in PANi matrix, thin films were found to be highly selective and sensitive towards NH3 gas at room temperature with a chemiresistive response of 70 % at 120 ppm and a recovery time of less than 120 s. The selectivity of the nanocomposite was also studied towards various reducing and oxidizing gasses. 2023 Wiley-VCH GmbH.
Karmakar N.; Jain S.; Fernandes R.; Shah A.; Patil U.; Shimpi N.; Kothari D.
https://doi.org/10.1002/slct.202204284 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159103067&doi=10.1002%2fslct.202204284&partnerID=40&md5=269e076553baf48ffe494fef12fae68b
Karmakar N., Dwarkadas. J. Sanghvi College of Engineering, Vile Parle, Mumbai, 400 056, India; Jain S., Department of Chemistry, Jai Hind College, Mumbai, 400020, India; Fernandes R., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Shah A., Department of Chemistry, University of Mumbai, Santacruz, Mumbai, 400098, India; Patil U., Wilson College, Chowpatty, Mumbai, 400007, India; Shimpi N., Department of Chemistry, University of Mumbai, Santacruz, Mumbai, 400098, India; Kothari D., Department of Physics, University of Mumbai Santacruz, Mumbai, 400098, India
Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks
ANN; fake profile classification; Fake profile detection; GAN; Machine Learning
Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms. 2023 IEEE.
Mahanta B.; Nair A.M.; Alapatt B.P.; George J.P.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Mahanta B., Christ (Deemed to Be University), India; Nair A.M., Christ (Deemed to Be University), India; Alapatt B.P., Christ (Deemed to Be University), India; George J.P., Christ (Deemed to Be University), India