Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model
Consumer behavior; e-commerce; Positive Word of Mouth; Relationship value; Satisfaction level; Trust value
The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. 2024 IEEE.
Gunavathi R.; Vijayalakshmi S.; Savithri M.; Thamarai Selvi S.B.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Institute of Electrical and Electronics Engineers Inc.
Gunavathi R., Christ University, School of Data Science, India; Vijayalakshmi S., Christ University, School of Data Science, India; Savithri M., Christ University, School of Data Science, India; Thamarai Selvi S.B., K.S.Rangasamy College of Technology, Computer Science and Engineering, India
Quantum Computings Path toSupremacy: Progress in the NISQ Epoch
GBS; Jiuzhang; Quantum information processing; Qubits; Random circuit sampling; Supremacy; Sycamore; Zuchongzhi 2.1
Quantum computing leverages the principles of quantum mechanics for information processing, with qubits serving as the fundamental units of quantum information. Qubits are quantum states where information processing can be engineered. Qubits possess the unique ability to encode, manipulate and extract information, enabling remarkable parallelism in computation. This enhanced computational speed, called quantum supremacy, promises to transcend established complexity boundaries. Significant strides have been made in demonstrating quantum supremacy through various experiments, most notably Googles 2019 experiment utilizing the Sycamore quantum processor to solve a problem that would stymie classical supercomputers for millennia. Other research groups, such as the Chinese team employing Jiuzhang and Zuchongzhi quantum processors, have achieved similar feats, showcasing the profound computational capabilities of quantum computers. It is essential to underscore that quantum supremacy does not signify quantum computers superiority across all tasks; current quantum computers remain constrained in their applicability, excelling primarily in specific problem domains. Nevertheless, recent advancements in quantum computing are noteworthy and ongoing development promises to expand their problem-solving capacities. This paper offers an introductory overview of quantum computing and an assessment of three prominent quantum supremacy experiments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1021 LNNS, pp. 315-325.
Springer Science and Business Media Deutschland GmbH
Puthussery E.S., Assumption College, Kerala, Changanassery, 686101, India; Poonia R.C., CHRIST University, Karnataka, Bengaluru, India
Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach
Academic Performance; Convolutional Neural Networks (CNN); Minimum Redundancy Maximum Relevance (mRMR)
The number of possibilities to analyze educational data using data mining techniques is expanding, with the goal of improving learning outcomes. There is an explosion in data produced by online and virtual education, e-learning platforms, and institutional IT. Using these statistics, teachers could gain valuable insights into their students' learning habits. Academic performance of students and other useful information can be analyzed with the help of educational data mining. Model training consists of three primary steps: data preprocessing, feature selection, and training the model. To eliminate unwanted problems like noise and redundant attributes, data preparation is necessary. By prioritizing which features to calculate, the mRMR algorithm lowers calculation costs. Feature selection plays a crucial role in training A-CNN-BiLSTM models. The suggested approach routinely outperforms BiLSTM and CNN, two state-of-the-art algorithms. With a data accuracy percentage of 96.57%, it's clear that there was a significant improvement. 2024 IEEE.
Nishant N.; Magadum A.; Pandey V.; Suganthi D.; Rashmi Bh.; Vigneshwaran K.
2024 2nd World Conference on Communication and Computing, WCONF 2024
https://doi.org/10.1109/WCONF61366.2024.10692223 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207377888&doi=10.1109%2fWCONF61366.2024.10692223&partnerID=40&md5=4d45f9a5dec15d1dd1f43576aaa5b829
Nishant N., Department of CSE, School of Engineering, Babu Banarasi Das University, Lucknow, India; Magadum A., Department of MCA, MIT-ADT University, College of Management, Maharashtra, Pune, India; Pandey V., Amity University, Patna, India; Suganthi D., Department of Computational Intelligence, Saveetha College of Liberal Arts and Sciences, SIMATS, Chennai, India; Rashmi Bh., School of Business and Management, Christ University, Bengaluru, India; Vigneshwaran K., Department of ECE, K.Ramakrishnan College of Engineering, Trichy, India
Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique
Big Data Architecture; Cardiac Disease Analysis; Convolution Neural Network (CNN); Deep Learning Algorithm; Image Processing; Machine Learning-Random Forest (ML-RF)
Cardiovascular diseases (CVDs) pose a significant global health challenge. Early and accurate diagnosis is crucial for effective treatment. This research focuses on developing a robust classification system for CVDs using machine learning techniques. This study proposes an enhanced Random Forest (RF) model optimized for big data environments and explore the potential of CNN-based classification. By leveraging medical imaging data and employing these advanced algorithms, we aim to improve the accuracy and efficiency of CVD diagnosis. 2024 IEEE.
Malarkodi P.; Arun M.; Manikandan R.; Ramkumar S.
5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024 - Proceedings, pp. 1598-1603.
https://doi.org/10.1109/ICESC60852.2024.10689896 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206639694&doi=10.1109%2fICESC60852.2024.10689896&partnerID=40&md5=0d01dabfbe59ef475b494e69e943a91d
Malarkodi P., Kalasalingam Academy of Research and Education, Department of Computer Applications, Tamilnadu, Krishnankoil, India; Arun M., Kalasalingam Academy of Research and Education, Department of Computer Applications, Tamilnadu, Krishnankoil, India; Manikandan R., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Ramkumar S., School of Sciences, Christ University, Karnataka, Bengaluru, India
Analysis of Nine Level Single-Phase Cascaded H-Bridge Inverters for EVs
Efficiency; Electric Vehicle Charging; Harmonic distortion; PV energy system; PWM
This paper explores the design and operation of a Modular Nine-Level Inverter (MLI)-Electric Vehicle (EV) charging system, incorporating solar energy to power domestic loads and charge EVs. The system comprises a solar panel, DC-DC regulator, and MLI for efficient energy conversion. The MLI's modular design reduces complexity and enhances efficiency. Equivalent circuits illustrate voltage level generation, while PWM control regulates power device switching for precise output control. Performance metrics, including regulated DC supply voltage and staircase nine-level output voltage, demonstrate the system's capability for diverse applications. A nearly sinusoidal current waveform and harmonic analysis underscore the system's effectiveness in delivering stable power with reduced harmonic distortion. Comparisons between filtered and unfiltered output highlight the importance of filtering techniques in improving power quality. Overall, the MLI-EV charging system showcases advancements in renewable energy integration, offering a versatile solution for sustainable electricity generation and EV charging. 2024 IEEE.
Parimalasundar E.; Jayakumar S.; Sita H.; Jayanthi R.; Hemanth Kumar B.; Suresh K.
2024 3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024
https://doi.org/10.1109/ICSTSN61422.2024.10671325 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205310396&doi=10.1109%2fICSTSN61422.2024.10671325&partnerID=40&md5=c0c2585d09eef4a5ece38497cd87b79a
Parimalasundar E., Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Sita H., Mother Theresa Institute of Engineering and Technology, Electrical and Electronics Engineering, Palamaner, India; Jayanthi R., Karpagam College of Engineering, Electrical and Electronics Engineering, Coimbatore, India; Hemanth Kumar B., Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Dept of EEE, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India
Secure Decentralization: Examining the Role of Blockchain in Network Security
Blockchain; Communication; Cryptographic; Decentralized; Preventing
Blockchain generation has emerged as a novel answer for securing decentralized networks. This technology, which was first created for use in crypto currencies, has received enormous interest in recent years because of its capability for boosting protection in various industries and community protection. The essential precept at the back of block chain technology is the decentralization of statistics garage and control. In a decentralized network, no central authority may control the statistics. Rather, the facts are shipped amongst multiple nodes, making it immune to tampering and single factors of failure. One of the most important advantages of blockchain in community protection is its capacity to offer cozy and transparent communication amongst community customers. Through cryptographic techniques, block chain can affirm the identities of network participants and ensure the authenticity of records trade. This feature is extraordinarily valuable in preventing unauthorized access and facts manipulation. 2024 IEEE.
Monga S.; Gupta P.; Logeshwaran J.; Thamaraimanalan T.
https://doi.org/10.1109/WCONF61366.2024.10692123 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207456667&doi=10.1109%2fWCONF61366.2024.10692123&partnerID=40&md5=199c35b5f68e5fd9fff4ac38e0a1a8bb
Monga S., Chitkara University, Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Gupta P., Chitkara University, Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Logeshwaran J., Department of Computer Science, CHRIST (Deemed to be University, Karnataka, Bengaluru, 560029, India; Thamaraimanalan T., Department of Electronics andCommunication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India
A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models
ALBERT; DistilBERT; Longformer; Machine Reading Comprehension; Span Extraction; Transformers
Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE.
Varghese N.; Shereef S.; Joy H.K.; Ramasamy G.; Sridevi R.; Cynthia T.; Rajeshkanna R.
Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies
https://doi.org/10.1109/CONECCT62155.2024.10677199 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205827823&doi=10.1109%2fCONECCT62155.2024.10677199&partnerID=40&md5=dd2387aa8f89cb758dc936c1f94ddd68
Varghese N., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Shereef S., Jain (Deemed to Be University), Department of Cs and It, Karnataka, Bangalore, 560059, India; Joy H.K., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Ramasamy G., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Sridevi R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Cynthia T., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Rajeshkanna R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India
Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification
brain tumor; classification; Deep learning; MRI
Classification is one of the primary tasks in data mining and machine learning which is used for categorizing data into classes. In this paper, brain MRI images are used for classification of tumors into three categories namely, Meningioma, Glioma, and Pituitary Tumor. These methodologies used are spatial based, depth based, feature map based and depth based CNN showcasing the power of deep learning in automating the tumor detection process. To evaluate the performance of several deep learning models, data is divided into training and testing data where a generalization method is used for comparison. The experimental results demonstrate promising accuracy, showing that a few techniques are valuable tools for radiologists and physicians, along with further analysis. The best accuracy obtained is 96% using MobileNet and ResNet50 in comparison to other CNN methodologies used in this paper. 2024 IEEE.
2024 IEEE 8th International Conference on Signal and Image Processing Applications, ICSIPA 2024
https://doi.org/10.1109/ICSIPA62061.2024.10685883 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206480084&doi=10.1109%2fICSIPA62061.2024.10685883&partnerID=40&md5=bea5a484da2dd58d7f6a394b7b97b344
Wahlang I., Ghani Khan Choudhury Institute of Engineering and Technology, Department of Computer Science and Engineering, Malda, India; Pohrmen F.H., Christ University, Department of Computer Science, Bangalore, India
Analyzing Dual-Stage Inverter Performance for Solar Grid Integration
Boost converter; Dual-stage inverter; Grid interaction; Photovoltaic; Utility grid
This paper presents a comprehensive analysis of the performance of dual-stage inverters in the context of solar grid integration through simulation. Dual-stage inverters are increasingly recognized for their potential to enhance the efficiency and reliability of solar power systems by mitigating grid disturbances and optimizing energy extraction. Through detailed simulation studies, this research evaluates key performance metrics such as grid stability, power quality, and energy conversion efficiency. The simulation environment enables the exploration of various operational scenarios and system configurations to assess the versatility and robustness of dual-stage inverter solutions. Furthermore, the study investigates the impact of control strategies and parameter variations on the overall performance of dual-stage inverters, providing valuable insights for system optimization and design. 2024 IEEE.
Parimalasundar E.; Jayakumar S.; Sita H.; Muthukaruppasamy S.; Suresh K.; Kumar B.H.
Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024, pp. 669-674.
Parimalasundar E., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Sita H., Mother Theresa Institute of Engineering and Technology, Electrical and Electronics Engineering, Palamaner, India; Muthukaruppasamy S., Velammal Institute of Technology, Electrical and Electronics Engineering, Chennai, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bengaluru, India; Kumar B.H., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India
Malpractice Detection in Examination Hall using Deep Learning
deep learning; Feature extraction; Human monitoring; Malpractice detection; YOLO
Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring. 2024 IEEE.
Aruna S.K.; Madhumitha A.; Shanmugam S.K.; Thangavel S.K.; Chang M.
Proceedings - 2024 2nd International Conference on Inventive Computing and Informatics, ICICI 2024, pp. 286-291.
https://doi.org/10.1109/ICICI62254.2024.00054 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206000518&doi=10.1109%2fICICI62254.2024.00054&partnerID=40&md5=621c8dff24fefd1f6d27ebb12a719188
Aruna S.K., CHRIST University, School of Engineering and Technology, Bangalore, Department of Computer Science and Engineering, India; Madhumitha A., CHRIST University, School of Engineering and Technology, Bangalore, Department of Computer Science and Engineering, India; Shanmugam S.K., Ashland University, Department of Mathematics and Computer Science, OH, United States; Thangavel S.K., Amrita Vishwa Vidyapeetham, Amrita School of Computing, Coimbatore, Department of Computer Science and Engineering, India; Chang M., Athabasca University, School of Computing Information and Systems, Canada
Specialized CNN Architectures for Enhanced Image Classification Performance
CIFAR-10 Dataset; Convolutional Neural Networks; Custom Architectures; Image Classification; Model Comparison
Image classification is one of the important tasks in computer vision, with a greater number of applications from facial recognition, medical imaging, object recognition and many more. Convolutional Neural Networks (CNNs) have developed as the foundation for image all classification tasks, showcasing the capacity to learn the hierarchical features automatically. In this study proposed three custom CNN models and its comprehensive analysis for the image classification tasks. The models are evaluated using CIFAR-10 dataset to assess the performance and efficiency. The experimental results shows that the proposed custom CNN Model-3 performance is better than the other two models. Our findings demonstrate that Model 3, featuring with the global average pooling, achieves the highest overall accuracy of 94 % with competitive computational efficiency. This suggests that global average pooling is the valuable technique for balanced and accurate image classification. 2024 IEEE.
Deepa S.; Jayapriya J.; Vinay M.; Kalpana P.; Suganthi J.; Loveline Zeema J.
2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024
https://doi.org/10.1109/NMITCON62075.2024.10699080 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207439676&doi=10.1109%2fNMITCON62075.2024.10699080&partnerID=40&md5=374f28bd5a9cff70da91fa7d936a0e59
Deepa S., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Kalpana P., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Suganthi J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Loveline Zeema J., Christ University, Department of Computer Science, Karnataka, Bangalore, India
Analysis of Routing Protocols in MANET Networks
Ad-hoc; B.A.T.M.A.N; Babel; BMX7; MANET; OLSRv1
The scientific article is a review and comparative analysis of routing protocols for MANETs. The study examines the main protocols connected to mobile ad hoc networks such as B.A.T.M.A.N, BMX7, OLSRv1, Babel and provides a detailed analysis of their characteristics, advantages and disadvantages. To empirically evaluate performance, tests were carried out in a network simulator. The results of the study allow us to draw conclusions about the effectiveness and reliability of each of the monitoring protocols under various operating conditions of MANET. This article is a valuable contribution to the field of MANET research and can be used in the development of new technologies and solutions for mobile wireless networks. The work is relevant and practically significant because it helps researchers and engineers make informed decisions when choosing the optimal routing protocol in MANET networks. The results obtained can be useful in the design of mobile applications, emergency communication systems, transport management and other areas where the efficient operation of wireless networks is important. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Krivolapova O.; Lapin V.; Shvedunenko D.; Anita E.A.M.
Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 141-151.
Krivolapova O., North Caucasus Federal University, Stavropol, Russian Federation; Lapin V., North Caucasus Federal University, Stavropol, Russian Federation; Shvedunenko D., North Caucasus Federal University, Stavropol, Russian Federation; Anita E.A.M., CHRIST (Deemed to be University), Bangalore, India
Design and Simulation of 6.2m Wide-Field Telescope for Spectroscopic Survey
Active Optics; Segmented Mirror Telescope; Spectroscopy; Survey; Wide Field
The upcoming large astronomical telescopes are trending towards the Segmented Primary Mirror due to technological advancements & manufacturing feasibility. We have designed a wide-field optical IR spectroscopic survey telescope that can deliver spectra of several millions of astronomical sources. The baseline design of this telescope is a 6.2 m segmented primary mirror with hexagonal mirror segments of 1.44m size, intersegment Edge sensors, and soft positioning actuators. The telescope is designed to provide a 2.5deg FOV achieved through a system of wide field corrector lenses with a design residual ~0.2". Also, it delivers an f/3.61 beam suitable for directly feeding optical fibres. A mechanical concept of the telescope is designed with a truss-based mirror cell to support the segmented primary mirror and keep the deformation to a minimum. As the primary mirror is segmented, the deformation due to different disturbances like wind, vibration and thermal effects must be corrected to a nanometer accuracy to make it act like a monolithic primary mirror. This is achieved through an active control system using three actuators and six inter-segment edge sensors. A simulation tool, codeSMT, is built based on the state-space model of a soft actuator with Multiple-Input Multiple-Output (MIMO) capability to incorporate dynamic wind disturbance from the IAO Hanle site and vibration effects. A detailed error multiplier analysis is performed numerically using this tool and is in good agreement with analytical calculations. A parameter sensitivity analysis is performed to fine-tune the primary mirror control system variables. This paper presents the Optical, Mechanical and Active Control system design approach of a 6.2m wide-field telescope currently under conceptual design. 2024 SPIE.
Deshmukh P.; Sriram S.; Chand T.; Kambhala S.; Ramya S.; Joshi R.; Bharat Kumar Y.; Muthhar M.
Proceedings of SPIE - The International Society for Optical Engineering, Vol-13094
Deshmukh P., Indian Institute of Astrophysics, Bengaluru, 560034, India; Sriram S., Indian Institute of Astrophysics, Bengaluru, 560034, India, CHRIST Deemed to be University, Bengaluru, 560029, India; Chand T., Indian Institute of Astrophysics, Bengaluru, 560034, India; Kambhala S., Indian Institute of Astrophysics, Bengaluru, 560034, India; Ramya S., Indian Institute of Astrophysics, Bengaluru, 560034, India; Joshi R., Indian Institute of Astrophysics, Bengaluru, 560034, India; Bharat Kumar Y., Indian Institute of Astrophysics, Bengaluru, 560034, India; Muthhar M., Indian Institute of Astrophysics, Bengaluru, 560034, India
Analysing the Influence of Activation Functions in CNN models for Effective Malware Classification
activation function; cnn; deep learning; malware classification
With the advancement of information technology, malware has become a persistent cyber security concern that targets computer systems, smart devices, and wide networks. Due to flaws in performance accuracy, analysis type, and malware classification methodologies that miss unsuspected malware attacks, malware classification has thus always been a significant concern and a challenging subject. Using the Malimg dataset, which has 9349 samples from 25 different families, this study classifies malware using a deep learning algorithm called a convolution neural network and evaluating the accuracy using a number of activation functions in this study. The proposed CNN model for malware classification achieves a high accuracy rate without the need for complex feature engineering. The model achieved the highest accuracy of 96.93% when using the Rectified Linear Unit (ReLU) activation functions whereas Leaky Relu gives accuracy of 96.76%, Pre relu gives 96.36%, ELU gives 95.72% and tanh gives accuracy of 95.58%. 2024 IEEE.
Kasliwal S.; Vinay M.; Jayapriya J.; Deepa S.; Mahalakshmi J.
2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024
https://doi.org/10.1109/ICDSNS62112.2024.10691109 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206574903&doi=10.1109%2fICDSNS62112.2024.10691109&partnerID=40&md5=0ceda2fbb8ecd648122c10a0bc505fa0
Kasliwal S., Christ Univesity, Department of Computer Science, Bangalore, India; Vinay M., Christ Univesity, Department of Computer Science, Bangalore, India; Jayapriya J., Christ Univesity, Department of Computer Science, Bangalore, India; Deepa S., Christ Univesity, Department of Computer Science, Bangalore, India; Mahalakshmi J., Christ Univesity, Department of Computer Science, Bangalore, India
Enhancing Small and Medium OEMs' Adoption of IIoT Technologies
Business support; Enhancing IIoT adoption; Industry Internet of Things manufacturer; Operational support; Small and Medium Original Equipment Manufacturers (SME OEMs); Strategic partnerships
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges of high initial costs, lack of skilled workforce, data security concerns, and limited infrastructure for IIoT implementation. This research explores the crucial factors influencing the successful integration of Industry Internet of Things (IIoT) technologies into products and processes of SME OEMs. The study investigates the impact of IIOT Manufacturers' operational and business support, training effectiveness, and awareness of benefits on SME OEMs' adoption intention of IIoT solutions. A survey was conducted among 263 firms operating in 103 different equipment manufacturing operations across 67 cities, representing 11 industry sectors. The participants were SME OEMs, and data were collected to assess the influence of various factors on their willingness to adopt IIoT technologies. The study revealed significant insights into adopting IIoT solutions among SME OEMs. Training provided by IIoT manufacturers was found to have the most substantial impact on the adoption intention. Moreover, awareness of benefits and business and operational support had an equal and notable influence on the adoption intention of SME OEMs. These findings underline the importance of effective training programs and comprehensive support from IIoT manufacturers in facilitating successful IIoT integration. The study's outcomes emphasize the value of fostering strategic partnerships between Small and Medium Original Equipment Manufacturers and Industry IoT Manufacturers. Such collaborations can be pivotal in enhancing IIoT adoption rates among SME OEMs, enabling them to stay competitive in the fast-paced market. 2024 IEEE.
Despande P.; Kumar Chandar S.; Elangovan N.
Proceedings - 2024 8th International Conference on Inventive Systems and Control, ICISC 2024, pp. 656-661.
https://doi.org/10.1109/ICISC62624.2024.00115 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206078510&doi=10.1109%2fICISC62624.2024.00115&partnerID=40&md5=d09e23a67937bbabf33a18d76657f90e
Despande P., Christ University, School of Business & Management, Karnataka, Bangalore, India; Kumar Chandar S., Christ University, School of Business & Management, Karnataka, Bangalore, India; Elangovan N., Christ University, School of Business & Management, Karnataka, 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
Artificial Intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention
Anti-Money Laundering (AML); Artificial Intelligence (AI); Machine Learning Algorithms; Suspicious Activity Reports (SARs); Transaction Monitoring
The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention. 2024 IEEE.
2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things, ICoICI 2024 - Proceedings, pp. 1010-1016.
Balaji K., Christ University, Business and Management, Bengaluru, India
Industry Internet of Things (IIoT) Adoption Pressures in SME OEMs
End-user Pull; Environmental Attitude; IIoT adoption; Institutional Pressure; Mediation Analysis; SME OEMs; Structural Equation Modeling
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges in IIoT adoption due to a lack of technical expertise, additional costs, and preferences of the end-users and significant institutional pressures. This research investigates the influence of Environmental Attitude on the Adoption Intention of Industry Internet of Things (IIoT) technologies among Small and Medium Enterprises and Original Equipment Manufacturers (SME-OEMs). This research analyses the effects of End-user Pull and Institutional Pressure in this relationship. A survey of 263 SME OEMs from 11 industrial sectors across 67 cities was conducted using purposive sampling. Structural Equation Modeling (SEM) was used to analyze data, assessing direct and indirect effects. Results show a significant positive relationship between Environmental Attitude and IIoT Adoption Intention. Mediation analysis reveals significant indirect effects through End-user Pull and Institutional Pressure, with complete mediation as the direct effect becomes insignificant. Findings highlight the crucial role of environmental attitude in shaping IIoT adoption intentions among SMEs. A positive environmental attitude drives SMEs to explore IIoT benefits. End-user Pull and Institutional Pressure are key mediators in this process. These insights are valuable for industry stakeholders, policymakers, and SMEs aiming to promote IIoT adoption. Fostering a positive environmental attitude and leveraging End-user Pull and Institutional Pressure can facilitate IIoT adoption. Policymakers can create initiatives to raise environmental awareness and encourage sustainable practices through IIoT. Industry players can form strategic partnerships to support SME OEMs in IIoT adoption. 2024 IEEE.
Proceedings - 2024 8th International Conference on Inventive Systems and Control, ICISC 2024, pp. 650-655.
Fire Resistance of Concrete with Partial Replacement of Ceramic Waste and Carbon Fiber as Additives
Carbon fiber; Ceramic waste; Fire resistance; Superplasticizer
One of the primary hazards that causes catastrophic damage to properties and peoples lives is fire. Although ceramic garbage is deposited on the land, it is a non-biodegradable waste that pollutes the environment. This study is based on the use of industrial waste products such as ceramic sanitary waste to improve the mechanical qualities of concrete that have been exposed to elevated temperatures. An experimental investigation was carried out on cubes, cylinders, and beams to assess compressive strength, split tensile strength, and flexural strength with fractional replacement of fine aggregates with 10, 20, and 30% of ceramic waste and 0, 1, and 2% of carbon fibers as additives at normal and elevated temperature as per ASTM code recommendations and the results shown as a significant improvement. The strength of M30 grade concrete with partial replacement of fine aggregate with ceramic waste up to 30% and carbon additives up to 2% shows an improvement of compressive strength by 17.56% than conventional concrete. It is also observed that normal M30 grade concrete loses its strength by 49.6% when it is exposed to 600C and with fractional replacement of fine aggregate by ceramic waste by up to 30% and carbon additives by up to 2% shows the loss of strength is decreased up to 22.67%. It shows that it is the probable substitute solution for the secure discarding of Ceramic waste. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Narasimha Murthy K.N.; Nelda Paul P.; Beevi Mymoon E.M.; Thejas H.K.
Lecture Notes in Civil Engineering, Vol-528 LNCE, pp. 621-631.
Narasimha Murthy K.N., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Nelda Paul P., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Beevi Mymoon E.M., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Thejas H.K., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India
Theoretical Framework for Blockchain Secured Predictive Maintenance Learning Model Using Digital Twin
Blockchain; Digital Twin; Predictive Maintenance
The automotive sector benefits from Digital Twins (DTs), software replicas of physical assets or processes. DTs enable engineers and data scientists to obtain deeper insights into the system and solve the most difficult problems faster and more affordably. Blockchain technology is a developing and exciting technology that has the potential to offer DTs monitoring capabilities, strengthening security and enhancing DTs transparency, dependability, and immutability. Intelligent behavior can be integrated into blockchain-based DTs to foresee important maintenance tasks and successfully manage machine functions. Our research involves creating a theoretical framework that leverages emerging technologies such as blockchain, artificial intelligence and DTs to facilitate resolution in the predictive maintenance of industry machines with minimised governing cost. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Vinodha D.; Jenefa J.; Anita E.A.M.; Babenko M.
Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 55-66.
Vinodha D., Departmentof Computer Science and Engineering, Christ University, Bangalore, India; Jenefa J., Departmentof Computer Science and Engineering, Christ University, Bangalore, India; Anita E.A.M., Departmentof Computer Science and Engineering, Christ University, Bangalore, India; Babenko M., North-CaucasusCenter for Mathematical Research, North-Caucasus Federal University, Stavropol, Russian Federation