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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems;however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT. © 2023 IEEE.

关键词： Internet of things

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Detectability Analysis of ai-generated Research Articles

Detectability Analysis of AI-generated Research Articles

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International Conference on computer and Information Technology (ICCIT)

作者： Neeraj Md Mokhlesur Rahman Md Lutful Kabir Sohag Kabir School of Computer Science AI and Electronics University of Bradford UK Department of Computer Science and Engineering Military Institute of Science and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The rapid surge of ai-generated content, driven by advanced large language models (LLMs) such as ChatGPT, threatens to undermine academic integrity and redefine the boundaries of plagiarism in scholarly work. This article evaluated the efficacy of five publicly available ai content detection tools in differentiating human-generated content from ai-generated ones. During the assessment, an ai-authored article was analysed using Sapling ai, Turnitin, GPTZero, Copyleaks, and Quillbot tools to assess their effectiveness in detecting ai-generated content. Following the detection reports, the paper was manually revised and resubmitted to the same tools to assess their ability to detect altered ai-authored content. This approach highlighted the susceptibility of these tools to modified texts and emphasised the difficulties in reliably detecting ai-created content. The analysis was conducted using the tools in their default state as black-box systems, without a thorough analysis of their underlying mechanisms or algorithms. Furthermore, the evaluation is confined to a single article on a specific topic, and as such, the performance of the tools assessed cannot be regarded as representative. To gain a comprehensive understanding of the effectiveness of these tools, further research and assessment involving articles from diverse disciplines and a variety of writing styles is essential.

关键词： Generative ai Large language models Plagiarism Writing Chatbots Reliability Artificial intelligence Surges Information technology Monitoring

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Fourier controller networks for real-time decision-making in embodied learning 24

Fourier controller networks for real-time decision-making in...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hengkai Tan Songming Liu Kai Ma Chengyang Ying Xingxing Zhang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Beijing China

Transformer has shown promise in reinforcement learning to model time-varying features for obtaining generalized low-level robot policies on diverse robotics datasets in embodied learning. However, it still suffers from the issues of low data efficiency and high inference latency. In this paper, we propose to investigate the task from a new perspective of the frequency domain. We first observe that the energy density in the frequency domain of a robot's trajectory is mainly concentrated in the low-frequency part. Then, we present the Fourier Controller Network (FCNet), a new network that uses Short-Time Fourier Transform (STFT) to extract and encode time-varying features through frequency domain interpolation. In order to do real-time decision-making, we further adopt FFT and Sliding DFT methods in the model architecture to achieve parallel training and efficient recurrent inference. Extensive results in both simulated (e.g., D4RL) and real-world environments (e.g., robot locomotion) demonstrate FCNet's substantial efficiency and effectiveness over existing methods such as Transformer, e.g., FCNet outperforms Transformer on multienvironmental robotics datasets of all types of sizes (from 1.9M to 120M). The project page and code can be found https://***/fcnet.

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Machine Learning Techniques for Protein Structure Prediction in Bioinformatics

Machine Learning Techniques for Protein Structure Prediction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Abdiqayumovich, Aliyev Nurillo Jaisudha, J. Al-Farouni, Mohammed Khare, Neha Kaur, Gurmandeep Koduru, Siva Sai Fergana Medical Institute Of Public Health Assistants Of The Department Of Biophysics And Information Technologies Fergana Uzbekistan Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Saveetha Institute of Medical and Technical Sciences Dapartment of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

This work analyses and discusses the use of machine learning methods in the field of bioinformatics concerning protein structure prediction. The steps of the proposed methodology include: acquiring and cleaning the relevant proteins sequences datasets;and creation and training of deep learning models, including Convolutional Neural Networks (CNNs), and Recurrent Neural Networks, (RNNs). Overall, adding data augmentation and transfer learning boosts the performance of the models to a mean accuracy of 87%. Predictors Evaluation: In evaluating the predictors, the model used an accuracy of 6% and a Matthews correlation coefficient (MCC) of 0. 74. The generality and efficiency of the proposed model are tested and compared with results from other similar standard datasets. Study shows that the results of the model have higher reliability than the traditional method of the machine-learning algorithm especially in the classification of the second and third levels of varied types of proteins. The research will demonstrate the applicability and possibility of the large-scale and cost-efficient approaches to the protein structure prediction using machine learning, while emphasizing the drawbacks of experimental tools. Recommendations for future work are to enhance the interpretability of the resulting models, better combine methods based on the use of information from hybrid schemes with physical models, and use more extensive databases for enhancing accuracy. This research highlights the profound impact that machine learning can have in the field of bioinformatics as a tool to enhance knowledge of such protein structures and to further practice improvements in the understanding of life processes and a and drug design. © 2024 IEEE.

关键词： Federated learning

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Machine Learning Face-Off: Performance Analysis for Breast Cancer Detection

Machine Learning Face-Off: Performance Analysis for Breast C...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammed Hamza Md Mokhlesur Rahman Md Lutful Kabir Sohag Kabir School of Computer Science AI and Electronics University of Bradford UK Department of Computer Science and Engineering Military Institute of Science and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Breast cancer (BC) is the most common cancer among women, underscoring the need for accurate and reliable early detection systems. Machine learning (ml) algorithms have shown great promise in healthcare by improving diagnostic methods for various diseases. This study evaluates the performance of several ml classifiers for breast cancer detection by analyzing ultrasound images. The classifiers tested include Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Neural Networks Multi-Layer Perceptron(NNmlP). The dataset was split into various training and testing ratios. Experimental results showed that the NNmlP classifier consistently achieved the highest accuracy, particularly in the 90/10%, 70/30%, and 60/40% splits. The SVM classifier also performed well, especially in the 70/30% split. The study concluded that the 70/30% split is the most effective, yielding the highest accuracy and balanced performance across multiple classifiers. Future studies should concentrate on optimizing hyperparameters, exploring ensemble methods, and enhancing the dataset with additional data augmentation to further boost model performance and reliability in breast cancer diagnosis.

关键词： Support vector machines Accuracy Ultrasonic imaging Artificial neural networks Data augmentation Breast cancer Data models Reliability Ensemble learning Tuning

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Enhancing Privacy in Real-Time Stream Processing: Federated Transfer Learning Approaches

Enhancing Privacy in Real-Time Stream Processing: Federated ...

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Metrology for Living Environment (MetroLivEn), IEEE International Workshop on

作者： Shwetha Jog Damodharan Palaniappan M. A. Jabbar Department of Computer Engineering Marwadi University Rajkot India Department of Information Technology Marwadi University Rajkot India Department of CSE-AI&ML Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350385014

ISBN: (纸本)9798350385021

In the era of data ubiquity, safeguarding privacy while harnessing the power of real-time streaming presents a critical challenge. This study delves into the intersection of privacy preservation, real-time streaming, and federated transfer learning to devise robust solutions. First, we elucidate the essence of data privacy, real-time streaming, and federated transfer learning. Subsequently, we propose an innovative approach that seamlessly integrates privacy considerations into the fabric of real-time streaming analytics through federated learning models. Leveraging this framework, we ascertain the optimal model selection process tailored to the specific data characteristics and user requirements. Our analysis encompasses a comparative evaluation of five prominent models: Differential Privacy, Secure Multi-party Computation, Homomorphic Encryption, Federated Learning with User-level Differential Privacy, and Decentralized Federated Learning. Through empirical examination, we discern the most suitable model for preserving privacy in real-time streaming contexts, thereby advancing the discourse on privacy-preserving machine learning paradigms.

关键词： Privacy Differential privacy Analytical models Federated learning Computational modeling Transfer learning Metrology

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Algorithmic Crypto Trading using EMA Strategy

Algorithmic Crypto Trading using EMA Strategy

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Revathi S Shobha Meesala Sudha V Priyanka R Manikandakumar M Vigneshwaran T Computer Science & Engineering (AIML) CMR Institute of Technology Bengaluru India Computer Science & Engineering REVA University Bangalore India Artificial Intelligence & Machine Learning Cambridge Institute of Technology Bangalore India AI ML and Data Science Christ University Bangalore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework.

关键词： Deep learning Pervasive computing Social networking (online) Profitability Navigation Data visualization Manuals Cryptocurrency Risk management Portfolios

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Dynamic Transfer Learning Switching Approach Using Resource Benchmark in Edge Intelligence

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IEEE Internet of Things Journal 2025年第13期12卷 25148-25170页

作者： Nkenyereye, Lionel Rajkumar, Chellakannu Lee, Boon Giin Chung, Wan-Young Pukyong National University Artificial Intelligence Convergence Education and Research Group Busan48513 Korea Republic of Pukyong National University Department of Electronic Engineering Busan48513 Korea Republic of University of Nottingham Ningbo China School of Computer Science Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute Ningbo315104 China Pukyong National University Department of AI Convergence Department of Electronic Engineering Busan48513 Korea Republic of

Machine learning (ml) techniques are applied for profiling computing and processing resources data collected while running deep neural network models on edge devices. Adaptive deep neural network (DNN) model switching requires proper benchmarking for categorizing ai models based on their applications and computational resources enabled by their processing accelerators. Based on benchmark metrics, DNN models can be classified into tiny, low, small, medium, and large resources, then identify DNN models that perform well within resource constraints. Ensure efficient resource allocation, latency management, and trade-off between accuracy and resource. In this work, we propose a benchmark for edge transfer artificial intelligence learning service (TALS) that uses ml techniques. They aim at classifying DNN models by their target edge applications while running edge inferences. We used both unsupervised learning (UL) and supervised learning (SL) techniques to identify the most effective features for the TALS models and to benchmark the performance of edge devices. To achieve this, two approaches were investigated: first, determining features based on edge inference’s computing resources profiling using principal component analysis;and second, classifying the DNN models at the target application level using a regression approach based on historical resource utilization data. In addition, we propose a dynamic model transfer learning that switches between a set of pre-trained and optimized and quantized DNN models based on the cost function. ml techniques learn resource-aware prediction from new resource allocation data and ensure that the multicriteria switch cost selects the inference task models that meet the edge resource constraint requirements. The experimental results highlight a strong relationship between the supervised learning model and the clustering execution method. The dynamic switching approach on real edge devices demonstrates dynamic switching between models

关键词： Adversarial machine learning

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Expression of Concern for: Building an Emotion Detection System in Python Using Multi-Layer Perceptrons for Speech Analysis

Expression of Concern for: Building an Emotion Detection Sys...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Regula Srilakshmi Vidya Kamma Shilpa Choudhary Sandeep Kumar Munish Kumar Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (数字)9798350342338

ISBN: (纸本)9798350342345

Human speech emotion recognition analyses a speaker's speech to determine their emotional state. Included are several applications in psychology, medicine, and human-computer interaction. Automated speech emotion identification techniques have been widely used in recent years thanks to machine learning algorithms. A number of speech processing libraries are available in Python, a popular programming language for system research. This focuses on creating a Python-based full speech popularity system that uses mlP classifiers. The goal of applications in psychology, healthcare, and human-computer interaction is to routinely discern the emotional state of a speaker based on their speech. With the aid of a multi-layer perceptron (mlP) classifier, we intend to improve a speech emotion reputation device written in Python. The proposed system will derive features from speech recordings, including mal-frequency cepstral coefficients (MFCCs) and prosodic functions. We will train and evaluate our model using an open-supply speech emotion popularity dataset, including RAVDESS. The extracted features may be supplied to an mlP classifier that has been trained using the classified emotional speech statistics dataset. Once mastered, the device can accurately categorise the speaker's emotional condition into a variety of categories, including happiness, despair, anger, and fear. Finally, we can evaluate the performance of our model using existing techniques on a distinct check out dataset.

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Building an Emotion Detection System in Python Using Multi-Layer Perceptrons for Speech Analysis

Building an Emotion Detection System in Python Using Multi-L...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

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