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SOA-based reservoir computing

SOA-based reservoir computing

引用

2024 international conference Laser Optics, ICLO 2024

作者： Bednyakova, A. Manuylovich, E. Ivoilov, D.A. Terekhov, I.S. Turitsyn, S.K. Novosibirsk State University Novosibirsk630090 Russia Aston Institute of Photonic Technologies Aston University BirminghamB4 7ET United Kingdom ITMO University School of Physics and Engineering St. Petersburg197101 Russia

ISBN: (纸本)9798350390674

We present a novel reservoir computing approach in which single nonlinear device - semiconductor optical amplifier, replaces the entire nonlinear reservoir to perform computations, without the conventional use of a delay loop. To study the performance of the proposed scheme, we use it for the benchmark prediction task of learning the Mackey-Glass chaotic attractor. © 2024 IEEE.

关键词： Semiconductor optical amplifiers

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Enhanced Skin Cancer Detection Model: A Deep learning Feature Fusion with Extreme learning Machine Approach 15

Enhanced Skin Cancer Detection Model: A Deep Learning Featur...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Hemantkumar, Parekh Ved Samal, Biswajeet Panda, Rudra Narayan Akhtar, Sk Shahid Muduli, Debendra Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

A major global health concern is Melanoma, a Malignant form of skin cancer. This type of skin cancer is the most aggressive and has the highest fatality rate. An early diagnosis and course of treatment is crucial. With impressive results in numerous dermatological analytic tasks, deep learning approaches have made it easier to identify, segment, and classify skin lesions in the previous few years. In this present work, several deep learning and machine learning based techniques are used to classify Benign and Malignant skin lesions. The models we propose have been trained and assessed on a manually curated dataset, composed of Melanoma and Benign lesion images derived from HAM10000, a comprehensive benchmark dataset that includes a variety of skin lesion types. Further, some additional images are selected from ***, which consists of a wide range of dermatology information services on the internet. These images enhance variability in our dataset, and result in better generalization of the models. The proposed work achieves the highest accuracy of 9 8. 0% for the validation set. © 2024 IEEE.

关键词： Deep learning Extreme learning Machine Melanoma Skin lesions Transfer learning

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Fraud Detection in Financial Transactions: A Machine learning Approach 9

Fraud Detection in Financial Transactions: A Machine Learnin...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350365092

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector. © 2024 IEEE.

关键词： Logistic regression

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Cloud computing Security: A Comprehensive Review of Machine learning Algorithms 2

Cloud Computing Security: A Comprehensive Review of Machine ...

引用

2nd IEEE international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Alsalami, Zaid Salim, Mohammed Hussein Al-Saidi, Imad Al-Din Alaak, H.H. Khaddour, Reem Hamdan Kareem Taaban, Nabaa Abdel Amir, Zahraa Naji The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Al-Farahidi University Baghdad Iraq Al-Kunooze University College Computer Engineer Department Basra Iraq The University of Mashreq Baghdad10021 Iraq Mazaya University College Thi-Qar Iraq Kut University College Business Administration Department Wasit Al Kut52001 Iraq Uruk University Baghdad Iraq

ISBN: (纸本)9798350368109

Utilizing the cloud implies having organized assets, particularly information and administration, accessible at whatever point you would like them without requiring uncommon client courses of action. More as of late, CC has developed into a arrange of both open and private datacenters advertising clients a single online course of action. By putting preparing and information capacity closer to the end clients, edge computing may be a forward-thinking approach to computers that guarantees to speed up reaction times and spare transfer speed. Versatile phone applications are dispersed by means of versatile gadgets that give scattered computing. By the by, security concerns with CC and edge computing, such deciding client defenselessness and organization together declaration, make it troublesome to rapidly distinguish compute models. Machine learning is the consider of computational processes on computers that truly gotten to be way better with involvement. In this investigate extend, we offer an examination of CC security dangers, issues, and strategies that utilize one or more ML calculations. Fortification, semi-supervised, uncontrolled, and synchronized learning are among the obvious machine learning computations that are surveyed in arrange to fathom cloud security issues. Following, we compare the points of interest, drawbacks, and focuses of intrigued of each methodology to decide how viably it was executed. Furthermore, we select to investigate up and coming ponders centered on secure CC models. Since cloud computing offers unmatched adaptability, adaptability, and cost-efficiency, it has totally changed how businesses store, process, and oversee information. But this fast acknowledgment has moreover brought up genuine security issues, requiring solid and intelligent security strategies to defend basic information. With the improvement of advanced strategies for danger location, inconsistency recognizable proof, and interruption avoidance, machine learning (ML) a

关键词： Cloud security

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DELCAS: Deep Reinforcement learning Based GPU CaaS Packet Scheduling for Stabilizing QoE in 5G Multi-Access Edge computing 23rd

DELCAS: Deep Reinforcement Learning Based GPU CaaS Packet Sc...

引用

23rd international conference on Web engineering (ICWE)

作者： Lee, Changha Lee, Kyungchae Cho, Gyusang Youn, Chan-Hyun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9783031503849;9783031503856

Recently, Docker Container as a Service (CaaS) has been provided for multi-user services in the 5G Multi-Access Edge computing (MEC) environment, and servers that support accelerators such as GPUs, not conventional CPU servers, are being considered. In addition, as the number of AI services is increasing and the computation power required by deep neural network model increases, offloading to edge servers is required due to insufficient computational capacity and heat problem of user devices (UE). However, there is a resource scheduling problem because all users' packets cannot be offloaded to the edge server due to resource limitations. To address this problem, we suggest deep reinforcement learning-based GPU CaaS Packet scheduling named as Delcas for stabilizing quality of AI experience. First, we design the architecture using containerized target AI application on MEC GPUs and multiple users send video stream to MEC server. We evaluate video stream to identify the dynamic amount of resource requirement among each users using optical flow and adjust user task queue. To satisfy equal latency quality of experience, we apply lower quality first serve approach and respond hand pose estimation results to each user. Finally, we evaluate our approach and compare to conventional scheduling method in the aspect of both accuracy and latency quality.

关键词： 5G MEC Deep Reinforcement learning Task Offloading

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Enhancing Credit Card Fraud Detection with Deep learning and Graph Neural Networks 15

Enhancing Credit Card Fraud Detection with Deep Learning and...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sherwin Akshay, J.G. Vinusha, T. Sharon Bianca, R. Sarath Krishna, C.K. Radhika, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Financial fraud poses a major threat to financial service institutions and clients, facilitating anomaly detection capabilities. This paper dives into deep learning models which can be implemented to detect and distinguish fraudulent transactions from normal transactions and flag anomalies. Further, conversion of tabular data to graphs have been implemented for keen understanding and improved pattern analysis of fraudulent transactions, challenges include evolving attack strategies, class imbalance and model interpretability. The proposed methods utilize a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like Auto-Encoders and Recurrent Neural Networks. In the graphical representation, this study focuses on implementing Louvain Modularity, Girvan-Newman and Label Propagation Algorithm for effectiveness in community detection. The outcomes exhibit consistently reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security. © 2024 IEEE.

关键词： Auto-Encoders CatBoost GAN GNN GRU RNN Unsupervised learning XG-Boost

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A Systematic Review of Data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using Machine learning Techniques 4

A Systematic Review of Data Augmentation Techniques in Natur...

引用

4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Patil, Uday Dhanaji Sudhakaran, Pradeep SRM Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Kattankulathur603203 India Kolhapur India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331515720

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. The main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Theoretical Models of Adoption and Acceptance of e-learning Prior and During Covid19 4

Theoretical Models of Adoption and Acceptance of e-Learning ...

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4th international Multidisciplinary Information Technology and engineering conference, IMITEC 2024

作者： Kolo, Ditlalane Irene Zuva, Tranos Applied Computer Sciences Vaal University of Technology Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Prior to the Covid19 pandemic, the educational methods used were physical face to face teaching, e-learning, and blended learning, which is a combination of physical teaching and e-learning. During the Covid 19 pandemic, physical education was not possible, leading to e-learning becoming the favorable method of administering education. While e-learning became the compulsory method during the Covid 19 pandemic, there was a limited time for adoption and acceptance of e-learning. This study reviewed the models and/or frameworks for adoption and acceptance prior and during Covid19 in e-learning to identify necessary variables for a pandemic period. The inclusion and exclusion criteria were used to select the papers assessed. The assessment acknowledged 18 related studies published prior and during Covid19. The results showed that prior to the pandemic period, there were variables used which were not included during the pandemic period in adopting and accepting the e-learning system. Out of 52 variables discovered in this study, 7 variables are confirmed to be common/core variables which are exogenous and/or endogenous. The 7 common variables are Social Influence, Facilitating Condition, Behavioral Intention, Perceived Usefulness, Perceived Ease of Use, Attitude and Intension to use. It further showed that Perceived Ease of Use and Perceived Usefulness were the most crucial common variables used during the pandemic. The core variables are indicators of adoption and acceptance of technologies in any environment. Therefore, it is concluded that the research studies are influenced by outbreak of disease pandemic or any natural disaster that can occur, as seen during the Covid19 pandemic the new variables were initiated. © 2024 IEEE.

关键词： Contrastive learning

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A Novel Trust Assessment System for Online Social Networking Environment Using learning Assisted Classification Model

A Novel Trust Assessment System for Online Social Networking...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nithya, S. Deepa, D. Babu, Vunnava Dinesh Pallavi, G. Fawareh, Hamed Kayalvizhy, R.D. Saveetha Engineering College Department of Computer Science Engineering Chennai India St. Joseph's College of Engineering Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Laxminarayan Innovation Technological University Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798331543617

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks. © 2024 IEEE.

关键词： Adversarial machine learning

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