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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Abhishek Bhuva Dipen Bhuva A. Hema D. Anandhasilambarasan G. Gowri Mukesh Soni Department of Computer Science University of Massachusetts Boston Boston Massachusetts USA Department of EECS Cleveland State University Cleveland Ohio USA Department of MCA E.G.S Pillay Engineering College Nagaipattinam Department of Computer science and Engineering Karpagam Academy of higher education Coimbatore Department of Artificial Intelligence and Data science Karpagam Institute of Technology Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cloud computing is becoming more and more popular. Many businesses are making investments in this field, either for internal usage or to offer services to external customers. Ensuring a secure and reliable environmental solution over cloud space is crucial for protecting and safeguarding the transactions conducted by organisations.A cloud computing adoption framework (CCAF) security that works well with business clouds is presented in this *** management, encryption, and firewalling are the three key security approaches that form the foundation of CCAF multilayered security. It is built on improvements in business file sync and share systems. The purpose of the methodology was to illustrate the CCAF multilayered security’ s resilience. In 50 hours of nonstop assault, CCAF multilayered security managed to identify and stop 99.7% of trojans and viruses, and achieve ≥ 80% blocking. The results of the research have been confirmed, and CCAF multilayered security can increase cloud-based big data services’ velocity, amount, and dependability.

关键词： Cloud computing Computer viruses Software as a service Robustness Encryption Security Trojan horses

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Deep Learning Approaches for Solving Challenges in Computer Vision Applications and Exploring Evolving Patterns

Deep Learning Approaches for Solving Challenges in Computer ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujay Gejji B. R. Supreeth Pathan Firoze Khan S. Venkat K Pradeepa S. Kaliappan Department of Electronics & Communication Engineering S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore Department of Computer Science & Technology Madanapalle institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was $81.6 \%$, the accuracy of GANs was $14.8 \%$, the accuracy of Hybrid CNN-RNN models was $86.2 \%$, the accuracy of ViTs was $79.2 \%$, and the accuracy of transfer for self-supervised learning techniques was $76.8 \%$. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality.

关键词： Computer vision Visualization Accuracy Computational modeling Surveillance Self-supervised learning Transformers Generative adversarial networks Object recognition Image classification

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Heart-disease prediction method using random forest and genetic algorithms 2

Heart-disease prediction method using random forest and gene...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： El-Shafiey, Mohamed G. Hagag, Ahmed El-Dahshan, El-Sayed A. Ismail, Manal A. Faculty of Computers and Information Technology Egyptian E-Learning University Dokki Giza12611 Egypt Faculty of Computers and Artificial Intelligence Benha University Department of Scientific Computing Benha13518 Egypt Ain Shams University Department of Physics Faculty of Science Abbasia11566 Egypt Faculty of Engineering Helwan University Helwan11731 Egypt

ISBN: (纸本)9781665418423

Today, heart-disease is one of the most significant causes of mortality in the world. Thus, the prediction of heart-disease is a critical challenge in the area of healthcare systems. In this study, we aim to select the optimal features that can increase the accuracy of heart-disease prediction. A feature-selection algorithm, which is based on genetic algorithm (GA) and random forest (RF), is proposed to increase the accuracy of RF-based classification and determine the optimal heart-disease-prediction features. The performance of the proposed approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the ROC curve by using a public dataset from the University of California, namely, Cleveland. The experimental results confirm that the proposed approach attained the high heart-disease-prediction accuracies of 95.6% on the Cleveland dataset. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods. © 2021 IEEE.

关键词： Forecasting

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Effective Classification for Logs of Routers Based on MLP Neural Networks

Effective Classification for Logs of Routers Based on MLP Ne...

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International Conference on Knowledge and Systems engineering (KSE)

作者： Bao-Thien Nguyen-Tat Duy Khanh Nguyen Faculty of Information Science and Engineering University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Vietnam

This article discusses the classification of abnormal log messages from Juniper routers. Abnormal log messages reflect the unusual activities of these routers. Classifying abnormal log messages into different categories helps monitoring personnel quickly identify errors and take timely measures to protect the devices. We extract features from abnormal logs and use a Multilayer Perceptron (MLP) neural network model for classification. The MLP neural network model requires knowledge of the log data from Juniper routers so that the trained network can analyze and validate that knowledge. We collected log data from Juniper routers operating on SCTV's core network system, identified abnormal log messages, and classified them based on our experience and knowledge.

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Parkinson’s Disease Detection using Hand Drawn Spirals and Waves

Parkinson’s Disease Detection using Hand Drawn Spirals and ...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Shashidhara H S Saish K Kalbag Tanya Mathias Srinivasa K G Niranjanamurthy M Department of Informaition Science and Engineering M S Ramaiah Institute of Techology and Engineering Affiliated to Visvesvaraya Technological University Belgaum Bangalore India Department of Data Science and Artififial Intelligece International Institute of Information Technology Naya Raipur Chhattisgarh India Department of Artificial Intelligence and Machine Learning B M S Institute of Technology and Management Affiliated to Visvesvaraya Technological University Belgaum Bangalore India

Parkinson’s disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson’s disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson’s disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson’s disease by analyzing hand-drawn spirals and waves in photographs.

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Large Language Models as Evolutionary Optimizers

arXiv

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arXiv 2023年

作者： Liu, Shengcai Chen, Caishun Qu, Xinghua Tang, Ke Ong, Yew-Soon Centre for Frontier AI Research A*STAR Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Frontier AI Research A*STAR Tianqiao & Chrissy Chen Institute United States Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology China Centre for Frontier AI Research A*STAR School of Computer Science and Engineering Data Science and Artificial Intelligence Research Centre Nanyang Technological University Singapore

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self-adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges. Copyright © 2023, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and artificial intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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Enhancing Cloud Security with AI: Developing Robust, Scalable Solutions for Threat Mitigation and data Protection in Cloud Platforms

Enhancing Cloud Security with AI: Developing Robust, Scalabl...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： AbuFaizur Rahman Abusalih Rahumath Ali K Saravanan B.Bala Abirami V.Samuthira Pandi Vijay Vasanth Aroulanandam S. Parthiban Enterprise Solution Architect Tekskills Inc. Princeton NJ USA Department of Information Technology R.M.K Engineering College Kavaraipettai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Networking and Communications Department School of Computing SRM Institute of Science & Technology Kattankulathur Tamil Nadu India Department of CSE SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

As cloud computing continues to gain popularity, guaranteeing the security of cloud environments has become an issue of the utmost importance for businesses all over the world. With a particular emphasis on the development of solutions that are both robust and scalable for the purpose of threat mitigation and data protection, this study investigates the application of artificial intelligence (AI) in the process of improving cloud security. Security frameworks that are driven by artificial intelligence are able to proactively discover, evaluate, and respond to new threats in real time. This is accomplished by employing machine learning algorithms and advanced analytics. With the purpose of demonstrating the efficacy of various artificial intelligence approaches in tackling common vulnerabilities such as data breaches, unauthorized access, and distributed denial of service attacks, we investigate a variety of AI methodologies, such as anomaly detection, behavior analysis, and automated incident response. In addition, we analyze the significance of a multilayered security approach, which involves integrating artificial intelligence tools with conventional security measures in order to develop an all-encompassing defense plan. The purpose of this article is to present case studies that illustrate successful implementations of artificial intelligence-enhanced security solutions on cloud platforms. These case studies demonstrate the influence that these solutions have on increasing security posture and operational efficiency. Ultimately, the findings of this study highlight the significant role that artificial intelligence plays in the transformation of cloud security practices. This paves the way for cloud infrastructures that are more robust and secure in the face of changing cyber threats.

关键词： Cloud computing security Prevention and mitigation data protection data breach Real-time systems Vectors Security artificial intelligence Faces Anomaly detection

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Automated Plant Leaf Classification using Ensemble Transfer Learning in CNN model

Automated Plant Leaf Classification using Ensemble Transfer ...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： S. Yuvalatha J. Keerthika S. Prabhavathy M. Banupriya R. Priyadharshini Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Department of Electronics and Communication Engineering Christ the King Engineering College Coimbatore Department of Artificial Intelligence and Data Science Hindustan Institute of Technology Coimbatore Department of Computer Science and Engineeeirng Sri Ranganathar Institute of Engineering Coimbatore

In the agriculture production, one of the biggest challenges is detecting the plant leaf disease in their early stages which significantly affects the farmer's earnings. In this paper various transfer learning CNN model of mobile NetV2, ResNet, InceptionV3, VGG16, Densenet are used to categories the potato leaf disease into healthy, Early blight, Late blight. data fragmentation strategies are applied to expand the number of images for the collected dataset to train the model. The mobileNetV2 achieve the accuracy of 86.8%. In this work the ensemble transfer learning for CNN model is proposed to identify the leaf disease. For the classification, majority voting ensemble techniques are used and obtained the accuracy of 94.8%.

关键词： Plants (biology) Transfer learning Production data models Agriculture Diseases

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Predictive Analytics for Proactive Maintenance in Industrial IoT Applications

Predictive Analytics for Proactive Maintenance in Industrial...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： A. Jaya Mabel Rani B. Yasotha Sevanthi P K. Karthika Y.Alexander Jeevanantham V.Samuthira Pandi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology KattankulathurChennai Tamil Nadu India Department of Artificial Intelligence and Data Science S.A.Engineering College (Autonomous) Thiruverkadu Chennai Tamil Nadu India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Electrical and Electronics Engineering RMK Engineering College Kavarapettai Chennai Tamil Nadu India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology(CAWit) Chennai Tamil Nadu India

Industrial Internet of Things (IIoT) enabled predictive analytics have enabled the manufacturing sector to transition from reactive to proactive maintenance. This article discusses the use of predictive analytics in the IIoT field of preventive maintenance. The objectives consist of increased output, availability, and decreased malfunctions-research on preventive maintenance cast light on its evolution. The proposed work presents a framework for IIoT data acquisition, cleansing, feature selection, and analytical processing. Experiments and case studies validate the methodology. Utilizing predictive analytics for routine maintenance can improve the effectiveness and productivity of machinery. data integrity, model consistency, and scalability must be resolved before implementation. The proposed work's findings and recommendations have significant implications for future research into predictive analytics for proactive maintenance in IIoT systems and for industry practitioners.

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