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machine learning-Based Security for Cloud Computing Challenges and Implications

Machine Learning-Based Security for Cloud Computing Challeng...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Rahul Dattangire Rushikesh Burle Divya Biradar Leelkanth Dewangan Independent Researcher Data Engineering Houston Texas USA Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Computer Science University of Texas at Arlington Arlington Texas USA G H Raisoni College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Cloud computing" is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost-cutting, advanced platforms, data processing, data analytics, and other uses. End users can use any online application from anywhere to access the cloud service providers' (CSP) services. It is imperative to guarantee the security of cloud infrastructure, and a multitude of research employing various technologies is used to provide more accurate and potent defenses against cloud threats. machine learning has shown to be a more useful technology in recent years for protecting cloud infrastructures. machine learning algorithms are trained to generate models that mechanize the process of recognizing cloud threats more precisely than any other technology by using a range of actual datasets as training grounds. A couple of the most recent research that has worked on machine learning to defend against cloud attacks are examined in this paper.

关键词： Training Cloud computing machine learning algorithms Data analysis Accuracy Computational modeling Reinforcement learning Organizations Computer security

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Impact of Ant Colony Optimization on Optimal Routing for Vehicular Ad Hoc Networks

Impact of Ant Colony Optimization on Optimal Routing for Veh...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Raghu Ramamoorthy Anitha Velu R China Appala Naidu Asha Kumari A Angel Donny F Harish Kumar M Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Vemana Institute of Technology Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

Using biologically inspired approaches has bonus advantages in offering optimal message distribution for vehicular ad hoc networks (VANETs). Bio-motivated approaches are motivated by the natural behavior of biological entities such as bees and insects. Ant colony optimization (ACO) is the optimal bio-inspired style, suitable for offering optimal routing in VANETs. The communication pattern of ACO and VANETs is similar, such as ants of ACO apply indirect communication among ants for the exchange of information, and VANETs require cooperative routing techniques for message exchange. The goal of the Bio-motivated approach is to attain proficient and accessible communication in a VANET, where vehicles turn as nodes and animatedly form a system to interchange information. In this context, the proposed work discusses the effectiveness and benefits of using a bio-inspired approach such as ACO for VANETs. To demonstrate ACO, a healthcare scenario is considered. In the healthcare system, efficient communication is required between emergency vehicles and hospitals to offer optimal treatment for patients. This work applies ACO to find the optimal path for sending an alarm to the nearest hospital from the emergency vehicle to arrange necessary things to treat patients. Using ACO increases the efficiency of the routing mechanism in all aspects such as throughput, overhead, data delivery, low latency, route discovery time, route repair time, convergence speed, and stagnation issue.

关键词： Ant colony optimization Hospitals Vehicular ad hoc networks Maintenance engineering Routing Throughput Routing protocols Biology Vehicle dynamics Convergence

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Integration of SAP and Intelligent Robotic Process Automation

Integration of SAP and Intelligent Robotic Process Automatio...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Thatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science WilliamsburgMO United States University of the Central Missouri Department of Computer Information Systems WarrensburgMO United States University of the Cumberlands Department of Information Technology WilliamsburgMO United States Jawaharlal Technological University Department of ECE Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350319088

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance. © 2023 IEEE.

关键词： Integration

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A Comprehensive Analysis of Secure Attack Detection on Virtual machines in Cloud Computing

A Comprehensive Analysis of Secure Attack Detection on Virtu...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Naveen Kumar Adalagere Nemirajaiah Channa Krishna Raju Department of Computer Science and Engineering Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumakuru India Department of Artificial Intelligence and Machine Learning Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumakuru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Cloud computing provides various computing services including processing power, storage, networking, software, analytics, and databases. A Virtual machine (VM) is a software-based simulation of a physical computer that runs an operating system and applications. The VM can run multiple functions on a single physical machine. Secure attack detection on VMs utilizes implementation techniques to detect and monitor malicious activities in the cloud environment. This detection involves Intrusion Detection Systems (IDS), anomaly detection methods, and real-time monitoring tools that analyse network traffic, system logs, and user behaviour. Optimization techniques are used to enhance the efficiency of the detection models. Swarm-based algorithms such as Intelligent Water Drop (IWD), Enhanced Shark Smell Optimization Algorithm (ESSOA), Brownian movement-centered gravitation search algorithm (BMGSA), Dual Conditional Moth Flame Algorithm (DC-MFA), Bat Optimization Algorithm (BOA), and Ant Colony Algorithm (ACO) are employed for this purpose. Additionally, Evolution-based algorithms such as Multi-objective Whale Optimization algorithm-based Differential Evolution (M-WODE), Gene Expression Programming (GEP), Genetic Algorithm (GA), and DragonFly-based Genetic Deep Belief Network (DGDBN) have also been used for anomaly detection. The performance metrics of makespan, energy consumption, Degree of Imbalance (DOI), and execution cost are used to evaluate these approaches.

关键词： Measurement Cloud computing Telecommunication traffic Virtual machines Software Real-time systems Optimization Anomaly detection Monitoring Genetic algorithms

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Zigbee Based Navigation of an Agricultural Robot Using Track Planning and Shape Recognizing Algorithm

Zigbee Based Navigation of an Agricultural Robot Using Track...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Chithra V Brindha S Girish S Mehul K Sachin M P Bharathwaj S Sri Sairam Engineering College Chennai India Department of Mechanical Engineering Sri Sairam Engineering College Chennai India Department of Information Technology Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Food production must increase dramatically due to a growing world population. Automation in robotic vehicles has a key role to play in improving farm productivity in agriculture field. Developing a robot capable of automatic ploughing saves time and increases accuracy. The zigbee based navigation will drive the ploughing robot to a desired direction, angle or degree to take turn in both forward and reverse direction in a particular track using vision based track planning and shape recognizing algorithm. A track is pre-planned and boundary points are marked in irregular agricultural land. Initially, the even areas of the land and the remaining uneven areas are marked into different sections. The robot vehicle will take a necessary turn when a bund (an obstacle) is detected by an obstacle sensor. The robotic vehicle will capture the image of an agricultural area in top view with the help of Wireless Camera which is mounted on the Vehicle Robot. By processing, vision-based track planning and shape recognising algorithm on the captured images, the accurate real time navigation of the robotic vehicle in a particular track on agricultural soil is achieved. A ploughing module on the rear end of the robot chassis enclosed with a ploughing arm and a ploughing tiller is attached which tills the soil to a reasonable amount of depth. Thereby, it reduces man power and time in ploughing the whole agricultural field.

关键词： Wireless sensor networks Automation Accuracy Shape Navigation Zigbee Soil Manipulators Planning Robots

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Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine based Ransomware Detection and Classification

Sine Cosine Reptile Search Algorithm with Grid Search Suppor...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Myasar Mundher Adnan S. Vaag Devi Deepthi VS Raghunathareddy MV Nagendar Yamsani The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning City Engineering College Bangalore Karnataka India Department of Computer Science and Engineering Cyber Security Dayananda Sagar College of Engineering Bangalore Karnataka India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Artificial Intelligence SR University Warangal India

The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and artificial Neural Network (ANN).

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Innovations in Voice Navigation Using Object Detection to Empower the Visually Impaired 3

Innovations in Voice Navigation Using Object Detection to Em...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Suryawanshi, Ranjeet Arudra, Annepu Dankan Gowda, V. Uthaman, Mathu Swathi Pai, M. Prasad, V.K. Satya Bharati Vidyapeeth's College of Engineering Department of Electronics and Telecommunications Engineering Maharashtra Kolhapur India Rajiv Gandhi Institute of Technology Department of Computer Science & Engineering Cholanagar R T Nagar Karnataka Bangalore India Bms Institute of Technology and Management Department of Electronics & Communication Engineering Karnataka Bangalore India Mahaguru Institute of Technology Department of Computer Science and Engineering Kayamkulam Alappuzha Kattachira India Nmam Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Nitte574110 India Symbiosis Institute of Business Management Hyderabad India Pune India

ISBN: (纸本)9798350319125

The project focuses on addressing the challenges faced by visually impaired individuals in navigating environments, especially when encountering obstacles or misplacing essential tools like walking sticks. A novel solution is proposed: a voice-controlled smart stick, designed to enhance the autonomy of visually impaired people in their daily activities without reliance on others. This research introduces a sophisticated walking stick equipped with ultrasonic sensors, operated via voice commands for ease of use and accessibility The voice-controlled smart stick is distinguished by its user-friendly interface, rapid responsiveness, minimal power consumption, lightweight design, and ease of folding and handling. This project not only offers a practical solution for the visually impaired but also signifies a significant advancement in assistive technology, enhancing independence and safety in daily life. © 2023 IEEE.

关键词： Autonomous Navigation Navigation Systems Object Detection Realtime Obstacle Detection Visual Impairment Voice Navigation

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Visionary Insights: CNN-Driven Retinal Vessel Segmentation in Ophthalmic Imaging

Visionary Insights: CNN-Driven Retinal Vessel Segmentation i...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sivagami, S. Gopinath, M. Yuktha Varshika, J. Srivarshinie, V. Monikaa, R. Senthil, K. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Information Technology Tamil Nadu Chennai602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Artificial Intelligence And Machine Learning Tamil Nadu Chennai602105 India Easwari Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai600089 India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Retinal vessel segmentation plays a vital role in the analysis of ophthalmic imaging. Binary Segmentation is one type of segmentation that segments the retinal vessel and blood vessel in retinal images. Retinal vascular geometry reflects a patient's state of health and aids in the diagnosis of certain conditions, including hypertension and diabetes. Early detection of these diseases helps to prevent blindness of patients. machine learning and Deep learning are two important sectors in artificial intelligence. Nowadays, Deep learning algorithms are very much used in Medical Image processing. These Deep learning Algorithms are more accurate and efficient than manual segmentation and other computer-aided diagnosis methods. Especially Convolutional Neural Networks (CNNs) in Deep learning Architecture are used for the segmentation process. CNN architecture can handle the complex characteristics of retinal images. Traditional Data augmentation techniques are used to enlarge the training and testing samples. Matched filter methods are used to remove the noise and Morphological operations are implemented to detect the abnormal regions in the retinal image. Our proposed model achieves an accuracy of 97.87%, precision of 96.8%, Intersection over Union of 97.1%, and specificity of 0.5% in the segmentation process. The proposed CNN architecture not only expedites the segmentation process but it makes advancements in clinical decisions in the field of ophthalmology. © 2024 IEEE.

关键词： Matched filters

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DeepLymphoDetect: Leveraging Deep learning Techniques for Acute Lymphoblastic Leukemia Detection in Blood Cells 1

DeepLymphoDetect: Leveraging Deep Learning Techniques for Ac...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Senthil, K. Monikaa, R. Gopinath, M. Vishwa, S. Yuktha Varshika, J. Sivagami, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Easwari Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600089 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Information Technology Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377002

Leukemia is a cancer that affects the bone marrow and blood, causing an excess of aberrant white blood cells (WBC). It can be affected by various factors such as Genetic mutations, Chemical exposure, and Immune system disorders. One kind of leukemia is acute lymphoblastic leukemia (ALL) that rapidly progressing and potentially life-threatening hematologic malignancy that predominantly affects children. This research employs a Convolutional Neural Network (CNN) architecture to propose an automated method for diagnosing and detecting acute lymphoblastic leukemia (ALL). For this research, Lymphocyte images are used as the input. The quantity of input samples is increased using a conventional data augmentation methodology, and correct findings are obtained by using a cross-validation method. This research achieves a 98.32% accuracy, a precision of 98.76%, and 97.38% recall for detection and classification tasks. This research exceeds the performance metrics of other cutting-edge methods. © 2024 IEEE.

关键词： Lung cancer

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A Review on the Use of AI and Robotics in Agricultural Practices

A Review on the Use of AI and Robotics in Agricultural Pract...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Mohan K. Warbhe Prateek Verma Rajendra Rewatkar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Robotics and artificial intelligence are becoming a part of the agricultural sector in ways bringing a change in it. It is evident that different technologies can help improve the yields, increase sustainability and efficiency in farming. This paper aims at offering a brief on the use of AI and robotics in agriculture; this will include using technologies such as Exact Hybrid Precision Farming, AI in livestock farming, AI-based resource supply chain applications, and others. In this literature review, the ideas of other authors are compiled and compare the concepts of culture as a factor in tackling the issues affecting agriculture and the possibility of utilizing artificial intelligence in the process. The theoretical literature review of the application of AI and robotics in precision farming aims at presenting literature evidence showcasing the use of activities such as predictive analytics, decision support systems, automated crop monitoring, and robotic harvesting. Other discussed issues are threats including high capital investment during the setting up and implementation of the system, replacement of labor by machines, as well as data insecurities.

关键词： Training Costs Bibliographies Government Supply chains Agriculture artificial intelligence Sustainable development Robots Farming

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