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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Nivetha R Ananya Bhattacharjee Suganya C Kamali S Pallavi P Sathana Sri C Sri Hari Lakshman M Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore Department of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper propose an artificial intelligence-based system that assists in improving badminton performance by correcting the posture with high accuracy. In contrast with traditional posture correction devices which usually depends on real-time feedback, this system focuses on post-game evaluation to provide complete and personalized insights. Using advanced video analytics and pose estimation (via MediaPipe), the system detects posture deviations, calculates joint angles and offers targeted corrective guidance. This process provides more granular, actionable feedback, compared to the broader stroke observation-based coaching. Additionally, the system also incorporates a progress tracking module, which provides a graphical representation of performance trends over time, thereby increasing user engagement and motivation. The user- centered design provides specialized feedback for all kinds of players from newbies to top experts providing scalability and adaptability. This data-Driven Technique helps in optimization of training rationale, reducing injury risks, and providing players with insights that ultimately helps them in refining their techniques.

关键词： Training Accuracy Target tracking Visual analytics Pose estimation Telecommunication computing artificial intelligence Optimization Injuries Sports

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DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

DTLLM-VLT: Diverse Text Generation for Visual Language Track...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xuchen Li Xiaokun Feng Shiyu Hu Meiqi Wu Dailing Zhang Jing Zhang Kaiqi Huang CRISE Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By leveraging high-level semantic information, VLT guides object tracking, alleviating the constraints associated with relying on a visual modality. Nevertheless, most VLT benchmarks are annotated in a single granularity and lack a coherent semantic framework to provide scientific guidance. Moreover, coordinating human annotators for high-quality annotations is laborious and time-consuming. To address these challenges, we introduce DTLLM-VLT, which automatically generates extensive and multi-granularity text to enhance environmental diversity. (1) DTLLM-VLT generates scientific and multi-granularity text descriptions using a cohesive prompt framework. Its succinct and highly adaptable design allows seamless integration into various visual tracking benchmarks. (2) We select three prominent benchmarks to deploy our approach: short-term tracking, long-term tracking, and global instance tracking. We offer four granularity combinations for these benchmarks, considering the extent and density of semantic information, thereby showcasing the practicality and versatility of DTLLM-VLT. (3) We conduct comparative experiments on VLT benchmarks with different text granularities, evaluating and analyzing the impact of diverse text on tracking performance. Conclusionally, this work leverages LLM to provide multi-granularity semantic information for VLT task from efficient and diverse perspectives, enabling fine-grained evaluation of multi-modal trackers. In the future, we believe this work can be extended to more datasets to support vision datasets understanding.

关键词： Visualization computer vision Annotations Conferences Semantics Natural languages Benchmark testing

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Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus 4

Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Tiwari, Ankita Babu, N. Ramesh Rakesh, Nitin Kumar, Mandeep Dhattarwal, Jagjit Singh Bhola, Abhishek College of Engineering Koneru Lakshmaih Education Foundation Department of Engineering Mathematics Guntur India School of Advnced Sciences Vellore Institute of Technology Department of Mathematucs Chennai India Department of Computer Science and Engineering Nagpur Campus Pune India School of Law Sharda University Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram India Chaudhary Charan Singh Haryana Agricultural University College of Agriculture Bawal India

ISBN: (纸本)9798350387490

Hazardous materials included in electronic trash (e-waste) can affect fetuses, neonates, and expectant mothers. Preterm birth, birth irregularities, and miscarriage are among the risks associated with pregnant women who are exposed to e-waste chemicals. Exposure to e-waste puts newborns at risk for respiratory and neurodevelopmental disorders. Developmental irregularities and growth restrictions are possible in fetuses. To reduce these risks and protect mother and child health, effective methods including appropriate e-waste discarding and public health programmes are important. © 2024 IEEE.

关键词： E-waste Environmental health Fetal development Neonatal health Neuro developmental disorders maternal pregnancy Public health policy recycling technologies Toxic chemicals

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Color Space Search Algorithm (CSSA): A Novel Optimization Approach

Color Space Search Algorithm (CSSA): A Novel Optimization Ap...

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Computational intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Nedal Nwasra Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel CSSA (Color Space Search Algorithm), introducing a metaheuristic optimization algorithm that addresses critical limitations in existing optimization methods. While current algorithms demonstrate suboptimal performance in multimodal landscapes, revealing premature convergence rates, CSSA introduces a novel approach by leveraging color theory principles in the optimization domain. The proposed algorithm combines complementary color transformations for global exploration with triadic color relationships for local exploitation, which aims to improve the exploration-exploitation balance compared to conventional methods. The algorithm's performance is evaluated across three categories of benchmark functions: unimodal functions, multimodal functions, and fixed-dimensional multimodal functions. Comprehensive experiments compared CSSA against classical (PSO, GA) and modern (MPA, EO, ALO, SSA, DA) optimization algorithms, with performance metrics including best fitness, mean fitness, solution consistency, and convergence rate analysis. Statistical analyses through the Friedman test (ranking 4.22) confirm the algorithm's robust performance, particularly in handling complex optimization landscapes. These results place CSSA as a promising optimization algorithm for complex optimization problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Optimization methods Color Evolutionary computation Benchmark testing Optimization Convergence

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Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

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作者： Liao, Beishui Liu, Fenrong van der Torre, Leendert Zhou, Yi School of Philosophy Zhejiang University Hangzhou310058 China The State Key Lab of Brain-Machine Intelligence Hangzhou310058 China Department of Philosophy Tsinghua University Beijing100084 China Department of Computer Science University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg School of Information Science and Technology University of Science and Technology of China HeFei230026 China

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Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms 23

Organs-At-Risk Segmentation in Medical Imaging Based on the ...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Lin, Xiao-Wei Li, Ni Qi, Qi School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Hainan Normal University Key Laboratory of Data Science and Intelligence Education of Ministry of Education Haikou China

ISBN: (纸本)9781665494571

In this paper, we proposed a new organs-at-risk segmentation method for Medical images of heads and necks based on the U-Net with residuals and attention mechanism. A Project & Excite (PE) block combined with a residual method is proposed as Residual-PE Block in the U-Net encoding stage in order to enhance the feature expression ability. Attention Gates (AGs) are introduced in the decoding stage to increase the relevance of feature weighting in accord with segmentation tasks. The proposed method has been evaluated through a set of real world medical images including 22 organs at risk in the head and neck. Experimental results showed that our proposed method improved the average segmentation accuracy by 3-4% compared with current existing methods. The average Dice Similarity Coefficient (DSC) score on the segmentation of the 22 types of head-and-neck related organs at risk is 79.5% and the average 95% Hausdorff Distance (HD) is 6.99mm. It demonstrates that this approach is promising for clinical doctors to improve their accuracy and efficiency in radiotherapy. © 2021 IEEE.

关键词： Image segmentation Head Three-dimensional displays Smart cities Medical services Logic gates Encoding

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by data Analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud Computing Point-of-Care Diagnostics

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Automatic Software Vulnerability Analysis through a Multi-Head Attention and Long Short-Term Memory Approach 2

Automatic Software Vulnerability Analysis through a Multi-He...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Lande, Jayapal Tejaswini, N.P. Al-Jawahry, Hassan M. Lekshmy, P.L. Sridevi, Mulagundla SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India The Islamic University Najaf Iraq LBS Institute of Technology for Women Department of CSE Trivandrum India CVR College of Engineering Department of CSE Telangana India

ISBN: (纸本)9798350317558

As Software Vulnerabilities continue to grow, they become a major aspect of software quality reflected in the increasing number of discovered and predicted code bugs. Analysts play a crucial role in localizing vulnerabilities yet security analysis still requires a significant manual effort to fix and repair functions. In this paper, a proposed method combines Multi-Head Attention (MHA) and Long Short-Term Memory (LSTM). This combination allows the model to handle variable-length input sequences a crucial capability for applications where input lengths vary. Such models can effectively learn to recognize patterns and dependencies in sequential data. However, the MHA model becomes more complex when interpreting the contribution of each head to the final prediction, posing a challenge, especially when dealing with limited training data. The increased model complexity from using MHA can lead to overfitting. The experimental results presented in the paper demonstrate high-level performance on the SARD dataset, achieving an accuracy of 99.00%. This performance surpasses other existing models, such as Deep Neural Network, Recurrent Neural Network, and Graph Neural Network. © 2024 IEEE.

关键词： Deep Neural Network Graph Neural Network Long Short-Term Memory Multi-Head Attention and Recurrent Neural Network

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Preferential Selective-Aware Graph Neural Network for Preventing Attacks in Interbank Credit Rating

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11414-11427页

作者： Junyi Liu Dawei Cheng Changjun Jiang School of Computing National University of Singapore Queenstown Singapore Department of Computer Science and Technology Tongji University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

Accurately assessing and forecasting bank credit ratings at an early stage is vitally important for a healthy financial environment and sustainable economic development. However, the evaluation process faces challenges due to individual attacks on the rating model. Some participants may provide manipulated information in an attempt to undermine the rating model and secure higher scores, further complicating the evaluation process. Therefore, we propose a novel approach called the preferential selective-aware graph neural network (PSAGNN) model to simultaneously defend against feature and structural nontarget poisoning attacks on Interbank credit ratings. In particular, the model establishes a phased optimization approach combined with biased perturbation and explores the Interbank preferences and scale-free nature of networks, to adaptively prioritize the poisoning training data and simulate a clean graph. Finally, we apply a weighted penalty on the opposition function to optimize the model so that the model can distinguish between attackers. Extensive experiments on our newly collected Interbank quarter dataset and case studies demonstrate the superior performance of our proposed approach in preventing credit rating attacks compared to state-of-the-art baselines.

关键词： Graph neural networks Banking data models Training Predictive models Optimization Perturbation methods Adaptation models Robustness Computational modeling

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ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-term Load Forecasting 2

ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-...

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2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Dong, Enchong Luan, Wenting School of Control and Computer Engineering North China Electric Power University Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China

Given the uncertain, random, and volatile characteristics of power load, accurate forecasting of power load becomes challenging. To address this issue, this paper proposes an optimized prediction model by using a modified Adaptive White Noise Complete Integrated Empirical Modal Decomposition Algorithm (ICEEMDAN) and the improved Whale Optimization Algorithm (IWOA) for bidirectional short-term and long-term memory networks (BiLSTM). First of all, Australian power load data is preprocessed in this study. Then, the load data is decomposed into a series of sequences by using ICEEMDAN. After that, the improved whale algorithm is employed to analyze the BiLSTM super parameter optimization. Finally, each decomposed component is input into the IWOA-BiLSTM model for load forecasting. The results demonstrate that the proposed algorithm achieves higher accuracy in power load forecasting, compared to individual benchmark models and most portfolio models, indicating its applicability and practical value. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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