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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Palei, Shantilata Lenka, Rakesh Kumar Behera, Santi Kumari Sethy, Prabira Kumar Nanda, Sandeep Barik, Rabindra Kumar Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar751003 India Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Sambalpur768018 India Electronics Sambalpur University Odisha Sambalpur768019 India Computer Science and Engineering Sanskriti University Uttar Pradesh Mathura281401 India School of Computer Applications Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India

ISBN: (纸本)9783031609343

Citrus disease is a major problem of economic and productive loss in agriculture worldwide. Crops are affected by uneven climatic conditions, which leads to lower agricultural yields. This will affect the world’s agricultural economy. This paper proposes an image recognition method based on machine learning (ML) and deep learning (DL) feature fusion techniques for citrus leaf disease prediction. The three most popular CNN models were considered. The CNN models were AlexNet, vgg16, and vgg19. The features of these CNN models were extracted and fed into the support vector machine (SVM) to classify citrus leaf diseases. These classification models were evaluated in terms of performance metrics calculation. Subsequently, the multilayer feature fusion technique was adapted to the best-performing CNN models. Here, vgg16 performed the best among AlexNet, vgg16, and vgg19. Then, the different combinations of feature fusion of vgg16 are adapted. These fused features were fed into the SVM for citrus leaf disease classification. The feature fusion of ‘fc6 plus fc7’ of vgg16 with SVM yielded the best results;that is, the maximum values of accuracy, sensitivity, specificity, precision, FPR, F1 score, MCC, and Kappa coefficient were 97%, 97%, 98.09%, 97.06%, 1.58%, 95.34%, 84.4%, and 86.79% respectively. This method is very much helpful to the farmer as well as to the customers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Citrus fruits

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A Regulatory Framework for Improving E-banking Security 15

A Regulatory Framework for Improving E-banking Security

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mishra, Komal Kaur, Amritpal Kaushal, Neha Kaur, Simarjeet Singh, Nongmeikapam Thoiba Bansal, Sunaina University Institute of Computing Chandigarh University Punjab India Chandigarh University Department of Computer Science And Engineering Punjab India Gulzar Group of Institutions Khanna Department of Computer Applications Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

This review paper delves into the intricate realm of e-banking security, examining regulatory frameworks, authentication methods, and emerging technologies. Through an extensive review of the literature, including multiple research papers and industry reports, common vulnerabilities and challenges in e-banking security were identified. These vulnerabilities are demonstrated through real-world case studies, which highlight the possible dangers connected to electronic financial transactions. The methodology involved in this review consisted of analyzing various scholarly articles, reports, and publications related to e-banking security, identifying recurring themes, and synthesizing key findings. The results of this review highlight the importance of understanding and addressing evolving security challenges in e-banking, ultimately equipping stakeholders with a holistic understanding to enhance security measures and mitigate risks effectively. © 2024 IEEE.

关键词： authentication cyber threats data encryption e-banking transport layer security (TLS) unauthorized access

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Neural Networks-Based Predictive Models for Self-Healing in Cloud Computing Environments 2

Neural Networks-Based Predictive Models for Self-Healing in ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sujatha, D. Michael Raj, T.F. Ramesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. St.Peters College of Engineering and Technology Department of Computer Science and Engineering Avadi Chennai India Sclas Smats Department of Computer Applications TamilNadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350306415

Due to the widespread adoption of cloud computing, protecting the security and uptime of cloud services is of crucial importance. Unforeseen malfunctions and performance bottlenecks in cloud settings can lead to considerable downtime and financial losses. Systems that can automatically detect, diagnose, and recover from errors using self-healing mechanisms are a promising new approach. This study investigates how neural network-based prediction models can be used to improve cloud-based systems' inherent capacity for self-healing. This research introduces a novel method of using neural networks to forecast cloud system failures and performance irregularities. The proposed prediction models are able to spot warning signs by assessing past data and current metrics in real time. Neural networks may accurately anticipate outcomes by recognizing complicated patterns and learning from large, varied information. Various forms of neural networks, including deep learning designs, are evaluated to determine their efficacy in diverse contexts. The study also looks into proactively reducing faults by incorporating these predictive models into self-healing systems. Responses, such as resource reallocation, workload migration, and backup procedures, are triggered automatically when problems are predicted. This preventative method drastically lessens the amount of time users spend waiting for their cloud-based systems to restart. Extensive simulations and real-world case studies yielded experimental data that prove the prediction models based on neural networks proposed for self-healing in cloud computing are effective. The models' proficiency in failure prediction enables corrective measures to be taken promptly and precisely. Implementing these predictive models in massive cloud environments is also discussed, as are their scalability, flexibility, and real-world consequences. by tapping into the potential of neural networks for predictive analysis, this study adds to the develop

关键词： Cloud Computing LSTM Neural Network Self-Healing

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TriBoost and Beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Katlariwala, Soham Biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, AdaBoost, Support Vector Machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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Federated Learning in the Era of Privacy Preservation: A Comprehensive Review and Future Directions 2

Federated Learning in the Era of Privacy Preservation: A Com...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kulshreshtha, Kushagra Goel, Bhawna Kanyal, Hoshiyar Singh Singhal, Vikas Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Abes Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Greater Noida Institute of Technology Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units. © 2024 IEEE.

关键词： Collaboratively Construction Federated Privateness Technology

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Cybersecurity Threat Detection in Smart Cities using Box Plot Sampling Isolation Forest 1

Cybersecurity Threat Detection in Smart Cities using Box Plo...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Shankari, S. Uma Mohameed, Haeedir Kulkarni, Megha Aravindh, S. Purushotham, N. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical Systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as Distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT-IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively. © 2025 IEEE.

关键词： Bot (Internet)

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Smart Health Management Application Using IoT and Smart Sensors 2

Smart Health Management Application Using IoT and Smart Sens...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Arnika Shaikh, Asmat Ara Kumar, Mukesh Sharma, Bhasker Yadav, Dinesh Kumar Aeri, Manisha Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Up Greater Noida India Parle Tilak Vidyalaya Association's Institute of Management Department of Marketing Management Maharashtra Mumbai India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India Hitech Institute of Engineering and Technology Department of Computer Science UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350371055

Clever fitness management software using the Internet of Factors (IoT) and smart sensors are a modern answer for powerful and personalized health care. This technology integrates innovative sensors with IoT, creating a community of connected gadgets that can music and screen a character's health and well-being. The utility collects facts in actual time, which are then analyzed and processed to provide valuable insights and recommendations for stepped-forward fitness management. The software uses diverse clever sensors, including health trackers, blood strain monitors, and glucose video display units, to constantly display the consumer's vital signs, symptoms, and physical activities. This record is then transmitted to the cloud for garage and analysis. © 2024 IEEE.

关键词： Garage Health care Management Sumptoms Transmitted

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P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 72-81页

作者： Ankayarkanni, B. Pani, Niroj Kumar Anand, M. Malathy, V. Bhupati Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Odisha Sarang India Department of ECE Dr M.G.R Educational and Research Institute Tamil Nadu Chennai India Department of ECE SR University Telangana Warangal India Department of IoT KL Deemed to be University Andhra Pradesh Vaddeswaram Guntur India

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fog computing

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