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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ahmed, Maqsood Ali, Raja Hashim Ali, Nisar Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Traffic Sign Recognition Systems (TSRS) are instrumental in improving road safety by assisting drivers and supporting autonomous vehicles in real-time identification of road regulations. These systems have advanced significantly in recent years, with deep learning approaches achieving promising results. However, despite these developments, existing models face challenges in adapting to the diverse conditions of real-world environments, including variations in lighting, weather, and sign appearance. Addressing this limitation, our study focuses on enhancing the robustness and accuracy of TSRS for practical deployment. In response to this gap, we introduce a novel deep learning model optimized for traffic sign recognition across various conditions. Our approach utilizes a convolutional neural network (CNN) architecture, which was trained on the German Traffic Sign Recognition (GTSR) dataset. We improved the model’s adaptability to new and unseen data while achieving a high accuracy rate of 97.99% by applying techniques like data augmentation and transfer learning. Methodologically, the model workflow includes extensive preprocessing, hyperparameter tuning, and real-time inference evaluations, ensuring suitability for deployment in autonomous driving systems. Our study contributes by providing a scalable, high-accuracy TSRS model that can reliably identify traffic signs in complex environments, supporting both autonomous and assisted driving technologies. The model’s performance paves the way for safer and more efficient road transport, while our results highlight the potential for further interdisciplinary research to expand TSRS capabilities. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches in Geomagnetic Storm Forecasting: A Comprehensive Survey and Future Prospects 2

Deep Learning Approaches in Geomagnetic Storm Forecasting: A...

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2nd International engineering Conference on Electrical, Energy, and artificial intelligence, EICEEAI 2023

作者： Khaled, Omar Salah, Shaimaa Attar, Hani Hafez, Mohamed Mohanad, A. Deif Elgohary, Rania Elsayed, Asmaa Misr University for Science and Technology Department of Artificial Intelligence Cairo Egypt Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Feqs INTI-IU-University Faculty of Engineering Nilai Malaysia

ISBN: (纸本)9798350373363

Geomagnetic storms significantly threaten modern technological infrastructure and communication systems, highlighting the critical need for accurate and timely forecasting. This survey research paper delves into the dynamic intersection of geomagnetic storm prediction and deep learning methodologies. This study provides a comprehensive overview of the current landscape in this field through a meticulous analysis of a diverse array of works, ranging from convolutional neural networks (CNNs) to recurrent neural networks (RNNs). The research scrutinizes the strengths and limitations of various deep learning approaches, shedding light on their efficacy in capturing the complex temporal and spatial patterns inherent in geomagnetic storm data. Notably, CNNs demonstrate promise in extracting spatial features, while RNNs excel at capturing temporal dependencies. Additionally, hybrid architectures combining these techniques exhibit a potential synergy in bolstering prediction accuracy. Furthermore, this paper underscores the pivotal role of data quality and diversity in training robust models. The availability of large-scale, multi-modal datasets is critical in enhancing predictive capabilities. Model interpretability and the ability to handle extreme events are pressing challenges warranting further investigation. The findings of this study hold far-reaching implications for space weather preparedness. Through the leverage of advanced machine learning techniques, our capacity to forecast geomagnetic storms is poised to be revolutionized, providing a crucial line of defense against their potentially devastating impacts on terrestrial technologies. The groundwork for future research endeavors is laid by this work, advocating for interdisciplinary collaboration between space scientists and machine learning experts. By addressing the identified challenges, a path toward more reliable and timely geomagnetic storm forecasts is endeavored to be forged, ultimately safeguarding our

关键词： Recurrent neural networks

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Tensor-Based Multi-Scale Correlation Anomaly Detection for AIoT-Enabled Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2061-2071页

作者： Zeng, Jiuzhen Yang, Laurence T. Wang, Chao Deng, Xianjun Yang, Xiangli Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada University of South China School of Electrical Engineering Hengyang421001 China

artificial intelligence of Things (AIoT) is an innovative paradigm expected to enable various consumer applications that is transforming our lives. While enjoying benefits and services from these applications, we also face serious security issues due to malicious cyber attacks on the massive growth of AIoT consumer devices. Accurate anomaly detection is one of the critical tasks for the trustworthy AIoT removing those obstacles. However, limited by the vector-based data pattern and ill-considered factors for anomalous samples analysis, existing methods suffer from the low detection performance. In this paper, a multi-scale correlation tensor convolutional Gaussian mixture network (named as MTCGM) is presented for ameliorating this actuality. Specifically, MTCGM suggests to construct the multi-scale correlation tensor by stacking one self-correlation matrix and multiple surrounding-correlations of different scales, which well characterizes the network status of AIoT. Subsequently, a 3D-convolutional autoencoder (3DCA) is designed for capturing inter-feature correlations, and followed with a Gaussian mixture probability (GMP) network for the observations likelihood estimation. Moreover, low-dimensional space features, relative Euclidean distance and tensor cosine similarity (TCS) are adopted in MTCGM as the multi-factor to boost the likelihood estimation. Extensive experiments on public benchmark datasets verify the validity of MTCGM, and demonstrate its superiority over the state-of-the-art baselines even in presence of contaminated training samples and input noise. © 1975-2011 IEEE.

关键词： Tensors

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Enhancing Fake News Detection Using BERT: A Comparative Analysis of Logistic Regression, RFC, LSTM and BERT

Enhancing Fake News Detection Using BERT: A Comparative Anal...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ramzan, Alishba Ali, Raja Hashim Ali, Nisar Khan, Ayesha Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

The research analyzed how to identify fake news effectively, understanding the developing capabilities to differentiate between misinformation and the correct nature of news in today’s complex media environment. Numerous approaches were tested: mostly the ways of determining if a certain message is valid or false. With application of Logistic Regression, Random Forest and lastly LSTM models, the research has been able to achieve 98.7%, 98.8%, and 95% accuracies respectively, showing that these traditional and advanced techniques are very effective. However, it became apparent that these models performed poorly as soon as tested on a new dataset and the discrepancy was not being able to understand context while being trained. Understanding the context is the key in news identification, so the research turned to BERT, the Transformer model pre-trained with huge contextual data to perform this task. Drawing on its deep and diverse knowledge base, BERT has shown a remarkable aptitude for sorting news articles into fake/real categories based on their context. Logistic Regression, Random Forest, and LSTM models demonstrated that although they were able to build models that were highly accurate up to 99% on familiar data, their accuracy dropped disproportionately as soon as new data was given. BERT, despite having a lower overall accuracy of 84% demonstrated a better sensitivity to contextual nuances in the news data. Here, the importance of contextualized conception in the sphere of fake news detection should be emphasized as a way to take advantage of BERT’s complicated comprehension which is a promising alternative of more accurate and effective identification as the media landscapes of the modern times may be complex and diverse. Despite the fact that the classical models surpass in planned environments, BERT’s capability of contextualization made it irreplaceable for evidence as the news sources in uncommon circumstances can digress from base. © 2024 IEEE.

关键词： Logistic regression

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SIGN-UNITE ASL Detection using ML for Deaf-Mute People Communication 5

SIGN-UNITE ASL Detection using ML for Deaf-Mute People Commu...

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5th IEEE International Conference on Advances in science and technology, ICAST 2022

作者： Kale, Pranali Nemade, Milind Karia, Aditya Majalkar, Vedshri K.J. Somaiya Institute of Engineering and Information Technology Department of Artificial Intelligence and Data Science Sion Mumbai India

ISBN: (纸本)9781665492638

There have been several advancements in technology and a significant amount of research has been done to help the people who are deaf and dumb. Aiding the cause, Deep learning, and computer vision has been used for detection of ASL to make an impact on this cause. We used linear SVM Algorithm and MediaPipe Libraries in our system that translates gestures like alphabets which is available for total 16 datasets A-K and U, V, W, Y with 99.6 to 99 percent accuracy then numbers from 1-10 with 99.83 percent accuracy and then 5 sentences with 99.6 percent accuracy. We attained overall 99.6 percent accuracy for detection of alphabets, numbers and words. © 2022 IEEE.

关键词： Libraries

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Empowering the Grid: A Comprehensive Review of artificial intelligence Techniques in Smart Grids

Empowering the Grid: A Comprehensive Review of Artificial In...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Elkholy, Marwa Shalash, Omar Hamad, Mostafa S. Saraya, M Sabry College Of Engineering & Technology Arab Academy for Science Technology and Maritime Transport Al Alamein Egypt College of Artificial Intelligence Arab Academy for Science Technology and Maritime Transport Al Alamein Egypt Arab Academy for Science Technology and Maritime Transport Research and Development Center Al Alamein Egypt Mansoura university Faculty of Engineering Mansoura Egypt

ISBN: (纸本)9798350351408

There are many operational and technical obstacles in the way of the shift to a decentralized, sustainable smart grid. In the face of growing renewable energy integration, distributed resources, and cyber threats, traditional grid management techniques are ill-suited to handle the real-time optimization, predictive analytics, and autonomous control necessary for dependable and efficient electricity delivery. This study thoroughly analyzes how artificial intelligence (AI) approaches can be used to address the main problems that smart grid systems face. The paper looks at how cutting-edge AI techniques, such as multi-agent systems, deep learning, machine learning, and optimization algorithms, can be used in important smart grid applications. © 2024 IEEE.

关键词： Smart power grids

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A Cognitive Architecture Based Conversation Agent technology for Secure Communication 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Preety Singh, Jagjit Yadav, Meenakshi Christ University Delhi NCR Ghaziabad India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Galgotias College of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multi agent systems

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EchoNet Dynamic: Deep Learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

Advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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Autonomous Capsule Maker & Filler 5

Autonomous Capsule Maker & Filler

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5th IEEE International Conference on Advances in science and technology, ICAST 2022

作者： Dhutraj, Dhananjay Nemade, Milind Dikonda, Mayur Gokhale, Shlok K.J. Somaiya Institute of Engineering and Information Technology Sion Department of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9781665492638

The present disclosure relates to a medicine manufacturing device and in particular to an autonomous device for making capsules and filling them with medicines which is used by the doctors (ayurveda and homopathic). Making their work in simpler form with the help of electronics devices such as linear actuator and motor, the manual work is replaced with the automation through the autonomous device and here we have also used the servo motor for opening and closing lid of the funnel. We are using agar agar, Hydroxypropyl methyl cellulose, gellan gum as main ingredients for capsule. Our mainly focus is filling part where we fill the medicinal powder in the bottom part of the capsule and encapsulation of capsule where we joint the two parts i.e top part and bottom part of capsule. Our objective was to implement 5 Capsules at a time (15min Estimated) © 2022 IEEE.

关键词： Filling

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