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1st international conference on computing and Emerging Technologies, ICCET 2023

1st International Conference on Computing and Emerging Techn...

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1st international conference on computing and Emerging Technologies, ICCET 2023

ISBN: (纸本)9783031776199

The proceedings contain 32 papers. The special focus in this conference is on computing and Emerging. The topics include: Automatic Invasive Ductal Carcinoma Detection Using Convolutional Neural Networks;intelligent Diagnosis and Assessment of Heart Patients Using Machine Learning;Automated Detection of COVID-19 Using Deep Convolutional Neural Network (CNNs) Using Chest Radiograph and CT Scan Images;development and Evaluation of Smartphones Interface for Elderly;cyber Physical systems Having Nodes/Layers with Trustworthy data: A Review;analysis of Device-To-Device Communication in Iot System;Extending WSN Lifetime with Cluster Head Election;Automatic HTP Assessment for Schizophrenic Patients Using Machine Learning;deep-Learning Based Multi-Modalities Fusion for the Detection of Brain-Related Diseases: A Review;human Activity Recognition Using Transformer Model;usability Impact of Conversational Chatbot;securing Drug Supply Chain with Medledger: A Blockchain Solution to Counterfeit and Hoarding Challenges;implementation of Authentic Resources for E-Learning in Education;securing Cyber-Physical systems by Using Artificial Intelligence;an Efficient Implementation of an IoT-Based Smart Home Security System;access and Secure Patient Medical Records Using Blockchain Technology Based Framework: A Review;subcellular Structures Classification in Fluorescence Microscopic Images;Bioelectric Signal acquisition and Design of Portable ECG for Medical Internet of Things Applications;classification of Cloud Attacks Using Deep Learning;facial Expression Recognition Using Convolution Neural Network;distinguishing Parkinsons Disease and Essential Tremors by Using Machine Learning;a Jurisprudence for Pakistan’s Digital Forensic Investigation Framework Regarding Cybersecurity;framework for Efficient Identification of Features Associated with Software Defects Using Spearman Correlation and Apriori Algorithm;segmentation of Brain Tumor Using U-Net Approach.

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advanced Tracking Algorithm for Multiple Dynamic Vehicles using Attention Mechanisms and Kinematic Models

Advanced Tracking Algorithm for Multiple Dynamic Vehicles us...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Kishore Ravikumar Kamal Sutaria Ganesh Pandit Pathak Shailesh Rastogi Department of Mechanical Engineering Chennai Institute of Technology Kundrathur India Deparment of Computer Science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of Modern Management Pune India Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Efficient vehicle tracking in urban environments is crucial for intelligent transportation systems but faces challenges such as nonlinear motion, dynamic backgrounds, and frequent occlusions. To address these complexities, we propose the AM-Vehicle-Track system, designed under the tracking-by-detection paradigm. This system integrates the Lightweight Convolutional Block Attention Module (LCBAM), which enhances detection accuracy by refining feature maps through channel-wise and spatial attention, and the Noise-Adaptive Extended Kalman Filter (NSA-EKF), which dynamically adjusts observation noise based on detection confidence to handle nonlinear vehicle motion. Additionally, the Byte data association technique is employed to mitigate issues with low-confidence detections and reduce identity switches. Tested on the challenging VisDrone-MOT dataset, AM-Vehicle-Track achieves a mean object tracking accuracy (MOTA) of 42.2%, an identity F1 score (IDF1) of 51.2%, and processes at 72 frames per second (FPS), outperforming existing state-of-the-art systems. These results highlight its robustness and efficiency in real-time scenarios, making it suitable for intelligent transportation applications. By significantly improving detection accuracy, tracking precision, and scalability, AM-Vehicle-Track establishes a new benchmark in multi-object tracking technologies and demonstrates its potential to enhance the reliability and efficiency of vehicle tracking in complex urban environments.

关键词： Accuracy Attention mechanisms Tracking Heuristic algorithms Urban areas Refining Real-time systems Kalman filters Vehicle dynamics Integrated circuit modeling

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Application of Deep Learning in Autonomous Mobile Robot Control: An Overview

Application of Deep Learning in Autonomous Mobile Robot Cont...

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Annual ieee systems conference

作者： Minh Nguyen Rickey Dubay Department of Mechanical Engineering Intelligent Controls Laboratory University of New Brunswick Fredericton New Brunswick Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Autonomous mobile robots (AMRs) are reshaping industries by automating tasks across diverse sectors, including logistics, healthcare, agriculture, and manufacturing. Recent advancements in deep learning (DL) have enhanced traditional control systems, empowering AMRs to process high-dimensional sensor data, improve perception, and navigate complex, dynamic environments. These techniques enable AMRs to perform a wide array of sophisticated tasks, including real-time navigation in crowded and cluttered spaces, dynamic obstacle avoidance, and accurate object recognition in settings like warehouses and agricultural fields. Beyond industrial applications, AMRs are also making significant strides in healthcare, particularly in elderly care, where they provide personalized assistance, help with mobility, and monitor health metrics through advanced perception and control mechanisms. This paper provides an overview of DL techniques in AMR systems, examining the roles of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL). CNNs are explored for visual perception tasks such as object detection, scene understanding, and localization. RNNs are utilized for processing sequential and time series data, such as inertial measurement units or force/torque sensors, to enhance scene perception. Finally, RLs are applied to decision-making and path planning in uncertain and dynamic environments. The paper also addresses the growing role of DL in overcoming key challenges in AMR systems, including enhancing robustness to environmental variations, enabling scalability across diverse operational scenarios, and improving autonomous decision-making capabilities.

关键词： Deep learning Scalability Decision making Medical services Robot sensing systems Real-time systems Safety Mobile robots Older adults Autonomous robots

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intelligent Computation in Healthcare using SDN-Enabled Network and IoT: Performance Enhancement and Efficiency Improvement

Intelligent Computation in Healthcare using SDN-Enabled Netw...

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2025 international conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Verma, Poonam Chandra Atulkar, Mithilesh Chouhan, Ravindra Kumar Nit Raipur Department of Computer Applications Raipur India Nit Raipur Central Computer Center Raipur India

ISBN: (纸本)9798331530259

The rapid growth of healthcare services, driven by digital technologies, has increased the demand for advanced computational and network infrastructures to handle complex healthcare applications. Custom topology network infrastructures struggle to meet growing data demands, leading to inefficiencies. Software-Defined networking (SDN) offers a flexible, scalable, and programmable solution that can significantly enhance performance and improve efficiency in healthcare systems. This paper focuses on implementing an SDN-enabled network in healthcare using a Ryu controller, Mininet simulation, and Iperf network performance evaluation tool. The benefits of SDN over custom topology networking technologies are analyzed in terms of network management, latency reduction, throughput improvement, and overall system efficiency, demonstrating clear improvement in healthcare system performance. The combination of IoT and SDN in healthcare enhances data flow, operational efficiency, and real-time decision-making. This integration improves patient outcomes, enhances operational efficiency, and is expected to significantly impact the industry as these technologies evolve. © 2025 ieee.

关键词： Decision making

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Smart Grid Tolerance Invasion Control Under False data Injection Attacks 8th

Smart Grid Tolerance Invasion Control Under False Data Injec...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Li, Ming Wei, Lisheng Wang, Ruiren Anhui Key Laboratory of Electric Drive and Control Anhui Polytechnic University Wuhu241000 China School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9789819602247

Aiming at the problem of how to maintain stable and secure operation of the smart grid after a false injection attack, the tolerant intrusion control of the system after a false data injection attack is investigated. First, since there is no publicly available FDIA dataset, two datasets are generated: one contains random single-node attacks and the other contains random multi-node attacks. Second, combining AE and Generative Adversarial Network (GAN), and introducing Wasserstein distance and gradient penalty term to avoid the problems of gradient vanishing and pattern collapse that GAN is prone to in adversarial training;at the same time, adversarial training is used to achieve Nash equilibrium, learn spatial relationship between data, and complete the data reconstruction;the method does not need labeled data from measurement instruments, and only requires attack-free historical state data to complete the training, thus it is unsupervised learning. Finally, the simulation results show that the adopted recovery scheme can accurately restore the contaminated state quantities to their pre-attack values, and has high recovery accuracy for FDIA contamination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhanced Anomaly Detection in Embedded Payment systems using Depthwise Separable CNN with Dandelion Optimizer

Enhanced Anomaly Detection in Embedded Payment Systems using...

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international conference on ent computing and Control systems (ICICCS)

作者： Sevinthi Kali Sankar Nagarajan Rabail Adwani Kardeepa Ponnuchamy Harshal Shah Shashikala Gurpur Ramya Maranan Independent Researcher 12460 Lanthimos San Antonio TX USA MS in Data Science Cranston RI USA Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Gujarat Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Vimannagar Pune Maharashtra Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Due to the prevalence of online payments, it is essential to identify instances of payment fraud. Plans that break rules and regulations within the payment systems not only cost money but also erode customer confidence. Finding these fraudulent transactions can be challenging because of the complexity of fraud and the sheer number of transactions. In this study, a new deep learning technique for identifying online payment fraud is proposed. The classification phase, hybrid feature selection, and sophisticated preprocessing form its foundation. Starting with the ieee CIS Fraud dataset and the PaySim Financial Credit dataset, the suggested approach is implemented. To ensure that all of the data sets have identical scales, a preprocessing step that uses Min-Max scaler normalization is utilized. The optimal qualities are picked using a hybrid approach that combines Gooseneck Barnacle Optimization (GBO) and the Artificial Hummingbird Algorithm (AHA). A depthwise separable convolutional neural network (DS-CNN) is then employed for classification, which facilitates the differentiation between fraudulent and non-fraudulent transactions. Additionally, to increase the detection rate, the Dandelion Optimizer (DO) optimizes the entire system. The aforementioned study indicates that the suggested approach has a high accuracy rate in detecting fraudulent transactions, which means it has almost infinite potential to speed up the entire process. In both datasets, the proposed method achieves an impressive 99% accuracy rate, proving its dependability and efficiency. This exceptional outcome shows how flexible the approach is to complex patterns and data variations, indicating its high level of reliability.

关键词： Accuracy Prevention and mitigation Feature extraction Regulation Fraud Complexity theory Classification algorithms Timing Convolutional neural networks Reliability

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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ieee international conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction data mining Facial features

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Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia

Towards Adaptive AI Governance: Comparative Insights from th...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Vikram Kulothungan Deepti Gupta Capitol Technology University Laurel Maryland USA Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk-tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence.

关键词： Economics Ethics Technological innovation Biological system modeling Asia Coherence Market research Regulation Security Artificial intelligence

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations

Exploring the Potential of LLM-based Chatbots for Task Sched...

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ieee international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Catarina Rema Armando Sousa Heber Sobreira Pedro Costa Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

关键词： Job shop scheduling Service robots Large language models Chatbots Real-time systems Mobile robots Resource management Reliability Autonomous robots Smart manufacturing

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Advancing Emotion Recognition through LLaMA3 and LoRA Fine-Tuning

Advancing Emotion Recognition through LLaMA3 and LoRA Fine-T...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Ibtissen Yacoubi Radhia Ferjaoui Warith Eddine Djeddi Anouar Ben Khalifa Université de Tunis El Manar Ecole Nationale d'Ingénieurs de Tunis Tunis Tunisie Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie Université de Jendouba Institut Supérieur d'Informatique du Kef Le Kef Tunisie Université de Sousse Ecole Nationale. d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisie

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Emotion recognition from text is a key task in natural language processing (NLP), with applications ranging from interactive chat systems and mental health detection to consumer feedback analysis. While various machine learning and deep learning techniques have been explored, the success of Large Language Models (LLMs) presents new opportunities revolutionizing how we interact with text. In this study, we harness LLaMA-3-8B for this task by applying LoRA (Low-Rank Adaptation), a highly effective fine-tuning technique that reduces computational and memory costs while maintaining high performance. Experiments on the ISEAR, Emotion for NLP and SemEval 2019 datasets demonstrate significant improvements in accuracy and efficiency over traditional approaches. This work highlights the potential of Llama-3-8B, combined with LoRA, to excel in complex language understanding tasks, particularly in emotion recognition.

关键词： Deep learning Performance evaluation Emotion recognition Adaptation models Translation Large language models Mental health Natural language processing Distance measurement data models

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