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Efficient Workload Distribution for Sustainable Server Utilization in Cloud Data Centers

Efficient Workload Distribution for Sustainable Server Utili...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Yadav, Monika Mishra, Atul J C Bose University of Science and Technology Ymca Dept. of Computer Engineering Faridabad India

ISBN: (纸本)9798350375237

Cloud service providers offer flexible infrastructure scaling options, allowing users to adjust resources based on their requirements and pay only for what they use. This paper proposes an approach to efficiently distribute workloads across host machines within a data center environment. This work proposes the concept of skewness as a measure of server utilization, identifying underutilized servers. When a server is found to be underused, its workload is redistributed to other host machines, enabling the underutilized server to be idled and potentially shut down. This strategy not only optimizes server utilization but also contributes to green computing initiatives by reducing power consumption and carbon footprint. Through Simulation, we demonstrate the effectiveness of our approach in achieving both scalability and sustainability in cloud computing environments. © 2024 IEEE.

关键词： Cloud computing

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Fault Tolerance Using AutoScaling in Amazon Web Services 1

Fault Tolerance Using AutoScaling in Amazon Web Services

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Nanthini, N. Shanmuga Prabha, P. Vidhyasri, R. Anand, Vruthi V. Sathyabama Institute of Science and Technology Dept. Computer Science and Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350365337

This study examines the design of fault tolerance mechanisms in cloud computing infrastructure, with particular emphasis on Amazon Web Services (A WS). Fault tolerance is critical to assure the continuous availability and reliability of cloud-based systems. Auto Scaling, an A WS feature, provides a dynamic way to handle demand changes while keeping the system flexible. This article presents a fault tracking management approach using Auto Scaling in A WS based on current research and real-world concerns. The importance of monitoring fault tolerance policies is emphasized, along with practical implementation methods. Furthermore, a case study demonstrating the success of this technique in real-world situations is presented, highlighting its importance for solving current challenges. © 2024 IEEE.

关键词： Websites

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Classification and Prediction of Stroke Disease Using Machine Learning Algorithms 7

Classification and Prediction of Stroke Disease Using Machin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Lahari, Podutur Kumar, Akarsh Nandini, D. Usha Praveena, M. D. Anto Selvan, Mercy Paul Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Using text mining tools and machine learning algorithms, the paper presents a prototype for classifying strokes. The significance of machine learning extends across various domains, including surveillance, medicine, and data management, where appropriately trained algorithms prove invaluable. The study incorporates data mining techniques, offering a comprehensive understanding of information tracking from both semantic and syntactic perspectives. By analyzing medical case sheets, the proposed approach extracts symptoms and trains the system based on that information. A total of 507 case sheets are collected at "Sugam Multispecialty Hospital"in "Kumbakonam", Tamil Nadu, India, during the data collection phase. A tagging and maximum entropy approach was used to mine these case sheets. Stroke classification relies heavily on the suggested stemmer for distinguishing between common and special features. To handle the data that has been processed, support vector machines, XG Boost algorithms, SGD algorithms, decision trees, and random forests were used as machine learning algorithms. Random Forest demonstrated the highest accuracy among stroke classification algorithms evaluated. This integration of text mining and machine learning offers a promising framework for enhancing stroke classification based on patients' symptoms extracted from medical case sheets. The synergy between these technologies provides a robust methodology that can contribute significantly to improving the efficiency and accuracy of stroke diagnosis and classification in a medical. © 2024 IEEE.

关键词： Semantics

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Reconsider Time Series Analysis for Insider Threat Detection

Reconsider Time Series Analysis for Insider Threat Detection

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Chia-Cheng Pao, Hsing-Kuo National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350362480

Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur

关键词： Contrastive Learning

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LSB based Image Steganography with Password-Protected SMTP Transmission 2

LSB based Image Steganography with Password-Protected SMTP T...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Prakash, Mangali Bhanu Kumar, E. Ravi Anitha, Banavath Bansilal, Boda Mukkamall, Babu Reddy Sanjana, Gudipati Pranaya Vardhaman College Of Engineering Dept. of Information Technology Hyderabad India Krishna University Machilipatnam Dept. of Computer Application Machilipatnam India

ISBN: (纸本)9798350388916

Picture steganography plays a crucial role in secure communication by discreetly altering images for various purposes such as data authentication, privacy protection, and watermarking. However, traditional methods often lack resistance to manipulation. To address this, we propose a solution that merges automated password generation with the Least Significant Bit (LSB) approach. This method involves embedding text within images and automatically generating an encryption password to safeguard data security. By employing Simple Mail Transfer Protocol (SMTP) credentials, encrypted photos can be directly sent to recipients, significantly expediting the process. This automated approach eliminates the necessity for separate transfers of the image and decryption password, thereby enhancing simplicity and security. This new technology seamlessly integrates encryption, ensuring a trustworthy and secure communication channel. With this innovative solution, users can transmit sensitive information confidently, knowing that it is shielded from unauthorized access or manipulation. By combining the strengths of automated password generation, LSB steganography, and SMTP transmission, our approach provides a robust framework for safeguarding digital communication in various context. © 2024 IEEE.

关键词： Authentication Protocol

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Semi-Stable Periodic Orbits of the Deterministic Chaotic Systems Designed by means of Genetic Programming 13

Semi-Stable Periodic Orbits of the Deterministic Chaotic Sys...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Matousek, Radomil Hulka, Tomas Lozi, René Pierre Kudela, Jakub Brno University of Technology Dept. of Applied Computer Science Brno Czech Republic Nice France

ISBN: (纸本)9798350308365

The aim of this paper is to show the possibility of generating general semi-stable periodic orbits using genetic programming (GP). This concept is a GP design of a perturbation sequence that forces a defined dynamical system to behave periodically. Recall that periodic orbits in deterministic chaotic systems are trajectories along which the system moves at regular intervals. Despite the chaotic nature of these systems, periodic orbits represent the repetition of certain states of the system over time. In chaotic systems, these orbits are usually surrounded by complex, irregular trajectories, but are themselves defined by regularity and predictability. We should add that periodic orbits are important to chaos theory because they provide a basis for understanding the internal structure of chaotic systems. Although chaos is defined by unpredictability based on initial conditions and the complexity, these periodic orbits represent islands of predictability that can be analyzed and modeled. GP and its symbolic regression capability is an ideal tool for finding both stable periodic orbits defined by stable points and general periodic orbits that rebuild attractive periodic states for the system. The objective function is also crucial for finding periodic orbits using GP. This function has been designed to achieve stable regions as well as the possibility of choosing the degree of the orbital. The test problem will consist of four systems of deterministic chaos, the so-called chaotic maps - the logistic map, the Henon map, the Lozi map and the Burgers map. © 2024 IEEE.

关键词： Genetic programming

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An Efficient Detection of Floating Marine Debris using Deep Learning Techniques

An Efficient Detection of Floating Marine Debris using Deep ...

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2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Pandey, Prachi Guru Prasad, M.S. Mayank, Kumar Gowda, Mahima M Prashanth Kumar, S.P. Karthik, D.U. Dept. of Computer Science and Engg. Dehradun India REVA University School of Computing and Information Technology Bengaluru India Acharya Institute of Technology Dept. of Computer Science and Engg. Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350344509

Growing concerns about the environmental consequences of floating debris in aquatic ecosystems have underscored the need for the development of efficient and automated methods for debris classification and monitoring. This research paper presents a comprehensive investigation into the application of neural networks, specifically Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM architectures, for this purpose. Our study entails the creation of a diverse dataset encompassing various debris types and environmental conditions, ensuring its real-world relevance and generalizability. Thorough exploration is conducted of the three neural network models to evaluate their effectiveness in classifying floating debris from images captured in aquatic environments. CNNs are chosen for their established image recognition capabilities, while LSTMs are incorporated to capture sequential information and potential temporal dependencies within debris trajectories. The outcomes of our study unveil the strengths and limitations of CNN, CNN-LSTM, and LSTM architectures in the context of floating debris classification. Proposed work provides insights into the suitability of each model in various real-world scenarios, including riverine systems, oceans, and coastal regions. Furthermore, we discuss the implications of our findings for environmental monitoring, debris removal strategies, and policy development. Throughout our research, each model's performance is assessed in terms of accuracy, precision, recall, and F1-score, considering the unique challenges posed by the inherently noisy and dynamic nature of aquatic environments. This research contributes to the evolving body of knowledge regarding the application of neural networks in environmental monitoring, with a particular focus on the critical domain of floating debris classification. Our findings are expected to guide the development of automated systems that can aid in mitigating the environmental i

关键词： Debris

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Smart Recruitment System 15

Smart Recruitment System

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

作者： Dugyala, Rakshitha Gaddam, Vinay Kumar Eroju, Harika Dantuluri, Mohana Varma Madhubabu, C.H. Bv Raju Institute Of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350370249

This project proposes the development of an advanced job applicants assessment and recruitment system that harnesses the power of artificial intelligence (AI) to streamline and enhance the hiring process. The system integrates cuttingedge AI algorithms, including natural language processing (NLP) and machine learning, to automate and optimize key stages of recruitment. The system begins with an AI-driven resume screening process that analyzes candidate resumes, extracting relevant information and evaluating qualifications against predefined criteria. Additionally, NLP algorithms are employed to comprehend the context and nuances of the resumes, enabling a more accurate assessment of candidates' skills and experiences. Furthermore, the system incorporates machine learning models to dynamically adapt and learn from past recruitment data, continually improving its ability to identify suitable candidates. This iterative learning process ensures a more effective and efficient selection of candidates over time. The intelligent-based system also facilitates automated interview scheduling, taking into account both candidate availability and interviewer preferences. By automating this process, the system optimizes the coordination of interviews, reducing the time and effort required from human resources personnel. Collaboration with HR professionals and data scientists is crucial for refining the system's algorithms and ensuring alignment with industry standards and ethical considerations. The proposed solution aims to revolutionize the traditional recruitment process by providing a robust, intelligent, and data-hy7driven approach to talent acquisition. © 2024 IEEE.

关键词： Artificial intelligence(AI) Data-driven approach Ethical considerations HR collaboration Intelligent recruitment Iterative learning Job applicants assessment Machine learning Natural language processing (NLP) Resume screening Talent acquisition

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Enhancing Low-Resolution Face Recognition with Self Attention Network and Resolution Augmentation Approach 2

Enhancing Low-Resolution Face Recognition with Self Attentio...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Soni, Ravindra Kumar Nain, Neeta Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331504106

Unconstrained low-resolution face recognition is a very hard and demanding task in the field of computer vision. Due to the scarcity of abundant visual features in low-resolution (LR) images, it is very challenging to extract discriminant key features to improve the identification performance of deep learning networks. The size of the LR images also poses a constraint on the recognition rate, and if it is tiny in size, then it hardens the recognition task more. For boosting the identification rate on unconstrained low resolution faces, a Residual Branch Self Attention Network (RBSaN) is designed. The RBSaN encompasses a self attention module where pixel-wise convolution is applied to channels for extracting the discriminant features. Resolution-augmented images are generated at multiple scales from the high-resolution (HR) images to capture the resolution aspects. RBSaN has achieved identification accuracy on the TinyFace dataset and Surveillance Cameras Face (SCface) database comparable to state-of-the-art (SOTA) models. Heat-map visualization witnesses the effectiveness of RBSaN to locate the key discriminant feature areas in low-resolution images for separating the identities in embedding space. Extensive experimental analysis has achieved qualitative results comparable to SOTA models. ©2024 IEEE.

关键词： Face recognition

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Expressing Liminality in Data within the Context of Deep Learning 5

Expressing Liminality in Data within the Context of Deep Lea...

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5th International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2024

作者： Mehmeti, Fatjon Elmazi, Donald Kulla, Elis IT University Metropolitan Tirana Dept. of Computer Science Tirana Albania Fukuoka Institute of Technology Dept. of Information Management Fukuoka Japan

ISBN: (纸本)9798331534530

In this paper we describe the concept of Liminality and how it manifests in Feature Spaces of Data during processes of Deep Learning, with particular focus on data during transition, transformation and other states where Liminality manifests in Data Feature Spaces and between various dimensions, Hyper-planes and Decision Boundaries. To go about this, we approached a decision boundary and gave it a threshold, which would behave as an event horizon, that way data can be measured to be in that threshold or out of it. In our work, we discovered that deploying solutions, by consulting a metric of liminality leads to an ability to understand whether a model being used is appropriate, or not, by seeing how its rearrangement, affects the data's proximity to the threshold. In summary, this paper addresses the problem of lack of dept. in the study of the evaluation of a Deep Learning algorithm, through the use of a liminality metric which measures which data is subject to certain techniques like feature selection, and pruning, or models that select progressively narrower sections of data from a given feature space. © 2024 IEEE.

关键词： Adversarial machine learning

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