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Security Risks and Their Mitigation Strategies: Cloud Computing Perspective 10

Security Risks and Their Mitigation Strategies: Cloud Comput...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Pandey, Sakshi Pathak, Prabhash Chandra Tripathi, Shubham Halwai, Sakshi Aggarwal, Shruti Nishant, Neerav Babu Banarasi das University Department of Computer Applications Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India School of Engineering Babu Banarasi das University Lucknow India

ISBN: (纸本)9798350382471

Cloud computing enhances the performance to the highest potential for cloud consumers, service providers, and cloud providers;it makes various services possible. The term 'cloud computing,' which refers to the most current advancement in internet-based computing, is very broad. The exploration of cloud computing has grown among businesses and government organisations. They can profit from the cloud computing model in a number of ways, including less capital and operational costs, with the aid of numerous relatively inexpensive computing entities and cutting-edge reference models, i.e., Platform as a Service, Software as a Service, and Infrastructure as a Service. Even though cloud computing has numerous advantages, its widespread adoption needs to be improved by several security issues, such as client and association vulnerabilities. This paper briefly overviews cloud computing, including its security threats and infrastructure problems. The technology of cloud computing is affected by these security threats. In addition, the influence of threats on security factors affecting cloud computing and cloud services is investigated. Also, it focuses on a few unique security countermeasures that can help people and businesses to avoid security threats in the cloud. © 2023 IEEE.

关键词： cloud computing common weakness enumeration (CWE) preventive measure security security risk

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Artificial Intelligence in Cybersecurity: Enhancing Automated Defense Mechanisms to Combat Sophisticated Cyber Threats and Guarantee Digital Resilience 5

Artificial Intelligence in Cybersecurity: Enhancing Automate...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prakash, V.S. Murugesan, P. Poongothai, P. Shivakumar, B.N. Vijayakumar, P. Shriidhar, P.J. Department of Computer Science Bengaluru India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Cmr Institute of Technology Department of Mathematics Bengaluru India Kongu Engineering College Department of Computer Applications Perundurai Erode India Department of Cse Salem India

ISBN: (纸本)9798350376685

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity approaches, with a growing emphasis on leveraging artificial intelligence (AI) to bolster automated defense mechanisms. This research delves into the integration of AI technologies within cybersecurity frameworks, exploring their potential to enhance digital resilience against sophisticated cyber threats. By examining the current landscape of cyber attacks and the limitations of traditional defense strategies, the study underscores the need for intelligent, adaptive systems capable of learning from vast datasets and responding in real-time to emerging threats. The research focuses on the development and evaluation of AI-driven defense mechanisms, including machine learning algorithms for anomaly detection, predictive analytics for threat forecasting, and natural language processing for cyber threat intelligence. Through a series of controlled experiments and case studies, the effectiveness of these AI-enhanced systems is assessed in terms of their accuracy, speed, and adaptability to new attack vectors. Findings indicate that AI-augmented cybersecurity solutions can significantly improve the detection and mitigation of advanced persistent threats, zero-day exploits, and other complex cyber attacks. Moreover, the research highlights the importance of ethical considerations and the responsible use of AI in cybersecurity to prevent the misuse of powerful AI tools and ensure privacy and fairness. The study concludes with recommendations for the strategic implementation of AI in cybersecurity, emphasizing the need for a holistic approach that combines human expertise with AI capabilities to achieve robust digital resilience. By fostering a collaborative ecosystem between cybersecurity professionals, AI developers, and policymakers, this research aims to contribute to the ongoing effort to safeguard digital infrastructure against the ever-evolving landscape of cyber threats. © 2024 IEE

关键词： Adversarial machine learning

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Segmentation of Retinal Pigment Epithelium Layer in Retinal OCT Images 5

Segmentation of Retinal Pigment Epithelium Layer in Retinal ...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sheeba, T.M. Antony Raj, S. Albert Anand, M. SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350305449

Optical Coherence Tomography (OCT) is a relatively recent procedure for studying eyes that has proven to be quite beneficial. We propose automatic detection of the RPE (Retinal Pigment Epithelium) layer in Retinal OCT images in this work. The bottom layer of the retina in retinal OCT images is known as the RPE layer, which is extracted in this study and may be studied to diagnose retinal disorders. After effectively increasing the quality of the OCT image using the Wiener filter, we extract the Retinal Pigment Epithelium layer using the Threshold approach. The proposed approach is tested using 40 OCT images of the retina. The results demonstrate 95.50 percent accuracy in RPE layer segmentation. © 2023 IEEE.

关键词： Optical tomography

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud Technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabakaran, T. Babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, B. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Enhancing Facial Expression Recognition with a Directional Attentive Neural Network 3

Enhancing Facial Expression Recognition with a Directional A...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Masson, Palak Agrawal, Rishi Sharma, Amit Parashar, Amit Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Greater Noida India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University School of Computer Application Phagwara India Pranveer Singh Institute of Technology Basic Science & Humanities-Chemistry Kanpur India

ISBN: (纸本)9798350384598

The proposed DANN was developed for better recognition of facial expression. The proposed DANN combines the attention and directional filters, which filter out features that focus on the informative facial regions while capturing the directional patterns in the facial queue. The average accuracy after benchmarking all the 10 experiment trials was in the very high level with the proposed DANN on datasets combining CK+, FER2013, and RAF-DB, with an average accuracy of 88.04%. The model showed a good trade-off between precision and recall: its average weighted F1 score was 0.881, while the average precision and recall were 0.883. This implies that the model will maximize the identification of true positive samples while, at the same time, minimizing that of false positives and false negatives. All these results are indicative of the robustness and reliability of the DANN architecture in classifying the facial expressions correctly across the different datasets and different experimental settings. The results suggest the high potential of DANN in furthering the state-of-the-art facial expression recognition with prosperous affective computing, human-computer interaction, and psychology. © 2024 IEEE.

关键词： Affective computing Attention mechanisms Deep learning Directional Attentive Neural Network Facial expression recognition

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Apple Fruit Flower Detection Using Auto-Encoder Convolutional Neural Network

Apple Fruit Flower Detection Using Auto-Encoder Convolutiona...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Yashi Rawat, Ruchira Shrivastava, Anurag Rashmi Bansal, Saloni Gupta, Priyanka Al-Fatlawy, Ramy Riad Lloyd Institute of Engineering & Technology Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Computer Science and IT Noida International University U.P. India Department of Computer Engineering and Applications GLA University Mathura India Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

The yield estimation task altogether relies upon the way toward identifying and checking the quantity of fruits on trees. In production of fruit, basic yield the board choices are guided by bloom intensity, i.e., the quantity of flowers presents in a plantation. The intensity of Bloom technique is still commonly assessed by methods for human visual investigation. Mechanized PC vision frameworks for flower recognizable proof depend close by designed procedures that work just under explicit conditions and with restricted *** work comprises of four significant advances, (I) System preparing for Fully Convolutional Network (FCN), (ii) Preprocessing, (iii) Component Extraction, (iv) Division. Initially, a strategy for assessing high-resolution pictures with deep FCN on Graphics Processing Unit (GPU). Then, secondly a non-linear and linear algorithms are presented for lessening the image noise, so the exact flower identification can be ensured. Thirdly, highlight extraction is valuable to diminish the quantity of assets required for handling without losing significant or applicable data. By applying Local Binary Pattern (LBP), surface example likelihood can be summed up into a histogram. At last, isolate a image with high resolution into sub patches, assess all patches with the help of AECNN, at that point apply the refinement calculation on acquired scoremaps to figure final version of the mask segmentation. Trial results are led utilizing two datasets on flower pictures of AppleA and AppleB. Results are estimated regarding the measurements like Precision(P) and Recall(R). © 2024 IEEE.

关键词： Graphics processing unit

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Predicting Glaucoma Severity Levels in Human Eyes: Integrating CNN and Random Forest for accurate classification 1

Predicting Glaucoma Severity Levels in Human Eyes: Integrati...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Dogra, Ayush Bhattacherjee, Abhishek Brar, Tejinder Pal Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Cgc Landran Department of Computer Applications Mohali India

ISBN: (纸本)9798350328042

This study prompts intervention and effective treatment, it is crucial to accurately categorize the various levels of glaucoma severity in human eyes. To accurately classify glaucoma sickness, this research introduces a novel methodology that combines Convolutional Neural Networks, also known as CNNs, and Random Forests synergistically. Our model displays amazing accuracy in differentiating between intensity classes by taking advantage of CNNs' skill at extracting complex information from medical pictures and Random Forests' talent for ensemble learning. The method includes careful data augmentation, preprocessing, and aggregation, which strengthens the model's durability. The effectiveness of the model over a range of glaucoma levels of severity may be understood by a thorough performance evaluation that takes variables like accuracy, recollection, and F1 score into account. The results highlight its potential as a tool for diagnosis for healthcare practitioners, enabling early detection. This study provides a transformational approach to improving glaucoma diagnosis and demonstrates the possibility of multidisciplinary collaboration between machine learning and medical field expertise. The results highlight the wider significance of technology-driven medical improvements in tackling complicated medical issues, with potential applications to other eye illnesses and beyond. © 2023 IEEE.

关键词： Health care

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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