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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Puri, Chetan Ghodeswar, Ujwala Reddy, K.T.V. Sharma, Mansi Warudkar, Sahil Department of Computer Science and Design Wardha India Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Nagpur India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331528713

Autism spectrum disorder (ASD) is characterized by neurological disorders and challenges with interpersonal communication, communication, and schedule behaviour. Early distinguishing proof of ASD is vital to optimize the intervention and progress long-term results. This paper analyses the potential of numerous machine learning procedures for early location of ASD in children. We propose a novel approach that combines gesture assessment with cognitive evaluation to capture a comprehensive picture of a child's behaviour. Side effect analysis will focus on highlights such as body language, hand movements, and eye contact, which can give profitable bits of knowledge into the social and communication designs of children with ASD. Therapy will utilize facial expressions, tone of voice, and body language to survey a child's emotional state. The combination of these strategies empowers the machine learning show to recognize complex structures that will be characteristic of ASD. This consider has the potential to move forward the exactness and productivity of ASD diagnosis in children. The proposed strategy can be connected in different situations, such as during routine testing or through a portable application. Early and precise conclusion can clear the way for convenient intervention and back for children with ASD, eventually driving to positive long-term results. © 2024 IEEE.

关键词： Diseases

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Cloud Storage and Secure File Sharing Using Blockchain with Xchacha20 and Shake Cryptography 16

Cloud Storage and Secure File Sharing Using Blockchain with ...

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16th IEEE International Conference on Computational intelligence and Communication Networks, CICN 2024

作者： Jayachitra, J. Arshad, H. Muthukumaran, R. Ifet College of Engineering Villupuram India Information Technology Ifet College of Engineering Villupuram India Artificial Intelligence and Data Science Ifet College of Engineering Villupuram India

ISBN: (纸本)9798331505264

Blockchain technology revolutionized record keeping for secure transactions but is not naturally file storage-oriented. This means that to share files securely and without central control systems, their secured decentralized sharing system needs to be developed. we propose an efficient file-sharing and storage system based on cryptographic hash function SHAKE-256 and end-to-end encryption using XChaCha20. The system highlights emphasis on privacy and integrity of data based on flexible access control policies for authorized users to access specific groups of data. Architecture will eliminate dependence on central storage solutions that are prime to vulnerabilities and single points of failure. Our solution offers a scalable and secure environment for applications in need of decentralized file storage by combining blockchain with more enhanced cryptographic techniques. Experimental results validate that the system is effective in keeping data confidential, integrity, and accessibility so it is eligible for privacy-sensitive use cases. © 2024 IEEE.

关键词： Blockchain Cryptography Decentralized Storage Secure File Sharing SHAKE-256 XChaCha20

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Machine Learning Diagnosis of Lumpy Skin Disease in Cattle Herds 3

Machine Learning Diagnosis of Lumpy Skin Disease in Cattle H...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Krishna, Nimmagadda Kumar Benjamin, Ruth Moly Tejaswi, Nimmagadda Institute of Technology and Sciences Division of Computer Science and Engineering Karunya Coimbatore India Shri Vishnu Engineering College for Women Division of Artificial Intelligence and Data Science Bhimavaram India

ISBN: (纸本)9798350375190

In order to diagnose lumpy skin disease in cattle herds, machine learning techniques such as Support Vector Machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective of the study is to create a diagnostic tool that would be both accurate and effective to identify and treat this infectious disease early on. The algorithms were trained to classify cases with high accuracy using a dataset that included different clinical and demographic characteristics related with the presence or absence of Lumpy Skin Disease. SVM is used because of its capacity to manage intricate and non-linear correlations in the data, and Random Forest and Gradient Boosting algorithms were chosen due to their predictive performance. © 2024 IEEE.

关键词： Support vector machines

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Predicting Flight Delays and Error Calculation Using Machine Learning Classifiers 5

Predicting Flight Delays and Error Calculation Using Machine...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajeshkumar, G. Vijaya, K. Sadesh, S. Arunkumar, P.M. Manoj Praveen, V. Kumar, M. Senthil Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Kongu Engineering College Department of Csd Perunduarai Erode India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Erode Sengunthar Engineering Collge Department of Artificial Intelligence and Data Science Perundurai India

ISBN: (纸本)9798350379945

Flight delays are a significant concern in the aviation industry, causing inconvenience for passengers and financial losses for airlines. Predicting flight delays accurately can help mitigate these issues by allowing both airlines and passengers to proactively manage schedules and resources. The delay might depend on weather, crew management, and resource planning. Existing prediction methods often lack accuracy or rely on historical data and statistical models, which may not capture the complex interactions and patterns present in flight operations to predict delays. This study proposes a novel approach for flight delay prediction using advanced machine learning techniques and tools. Specifically, machine-learning classifiers, such as Support Vector Machines (SVM) and Decision Tress, are employed to analyze a comprehensive live streaming dataset comprising various incorporated factors like flight operations, weather conditions, air traffic congestion, and aircraft performance. Proposed model is trained on a large dataset encompassing diverse routes and airlines and has achieved an accuracy of 95% in predicting delays by outperforming existing methods. © 2024 IEEE.

关键词： Traffic congestion

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IoT-Based Smart Wearable Devices Using Very Large Scale Integration (VLSI) Technology 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Ashwin, M. Naidu, R. Ch. A. Ramamoorthy, Raghu Kumar, E. Saravana Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Science and Engineering The Oxford College of Engineering Karnataka Bengaluru India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

People’s usage of smart wearable devices and sensors plays a crucial role in VLSI technology. The wearable devices are embedded in clothes, smartwatches, and accessories. The wear gadgets like smart rings, smartwatches, and smart spectacles are associated with human healthcare monitoring, actual period location discovery and portable online games, etc. This paper presents a survey of the recent usage of wearable devices and sensors with VLSI technology. Three key features considered to make user-friendly smart wearable devices: They are security, performance, and deployment cost. Almost all wearable devices are equipped with Bluetooth communication and screen displays like smartwatches and smartphones. The primary goal of this research is to minimize the power consumption for wearable devices. Smart wearable devices are utilized for a wide range of applications like medicine, sports, fitness, business, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wearable sensors

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Remote Sensing Image Change Detection Based on Similarity Sensing Network 5

Remote Sensing Image Change Detection Based on Similarity Se...

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5th International Conference on Computer, Big data and artificial intelligence, ICCBD+AI 2024

作者： Chen, Jin Li, Jian-Yu Xu, Jing Shao, Zhao-Kun Xu, Xin-Xin Zhan, Zhi-Hui Nankai University College of Artificial Intelligence Tianjin China Hanyang University College of Electrical and Electronic Engineering Ansan Korea Republic of Victoria University of Wellington Centre for Data Science and Artificial Intelligence Wellington New Zealand

ISBN: (纸本)9798331533991

Remote sensing image change detection has shown its potential in various applications such as ecosystem detection and climate emergency response. However, existing change detection methods fail to adequately address the gradient disappearance caused by class imbalance and ignore the similarity between the dual-temporal images, thereby limiting their learning ability. Aiming at these issues, this paper introduces the ideas of metric learning and attention mechanism into the deep learning network, and proposes a remote sensing image change detection model with two novel designs. First, a similarity-guided attention mechanism is introduced into each stage of the encoder and decoder to adjust the feature weights of different regions, which can better capture the subtle changes of ground cover. Second, the batch-balanced contrastive loss is used to optimize the learning effect of the model on rare categories and difficult samples. In the experiments, the proposed model shows high learning performance and anti-interference ability, which provides an efficient tool for handling change detection tasks with remote sensing images. © 2024 IEEE.

关键词： Contrastive Learning

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Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Addressing data Imbalance in Machine Learning: Challenges and Approaches 6

Addressing Data Imbalance in Machine Learning: Challenges an...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Zade, Nikita Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India

ISBN: (纸本)9798331522667

A key difficulty of dealing with imbalanced datasets is that machine learning models are trained to favor the majority class and therefore perform poorly when it comes to a minority class classification. In this paper, we consider the following methods of tackling the issue of the over-representation of a class;random oversampling, undersampling, SMOTE, cost-sensitive learning, and ensemble methods. Comparing them, we assess the performance of each of these techniques within different classifiers. Techniques including SMOTE together with combined methods including Random Forests also show higher capacity in minority class predictions. However, problems such as overfitting and the increase in the computational load are not escapable yet. We briefly recap the strengths and weaknesses of each approach and propose potential refinements including a combination of both methods and dynamic resampling methods to serve as the next steps toward improving the model. Thereby, our results draw attention to the necessity of accurate methods for handling the class imbalance, which will further yield more accurate and fair machine learning solutions across a variety of applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Dual Attention and Question Categorization-Based Visual Question Answering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2023年第1期4卷 81-91页

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science & Engineering Guwahati781039 India Department of Electronics and Electrical Engineering Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities;second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space;and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance. © 2022 IEEE.

关键词： Forecasting

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MTTM: Memory-Augmented with Mamba for 3D Medical Images Analysis

MTTM: Memory-Augmented with Mamba for 3D Medical Images Anal...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Hongkai Yang, Yang Sun, Shijie Song, Huansheng Guo, Keyu Bu, Yongfeng School of Information Engineering Chang'an University Xi'an China School of Data Science and Artificial Intelligence Chang'an University Xi'an China

ISBN: (纸本)9798350368741

The rapid advancement of artificial intelligence has propelled the healthcare industry into a new era of diagnostic precision. A pivotal component of this evolution is the accurate classification of 3D medical images, which necessitates extracting robust feature representations capable of effectively modeling long-range dependencies within the data. This paper introduces the Mamba Token Turing Machine (MTTM), a novel architecture that integrates the efficiency of Mamba with the memory mechanisms of the Token Turing Machine (TTM), effectively addressing limitations of Transformers in long-range dependency modeling. MTTM's Memory-Augmented Processing Unit (MAPU) employs four blending methods, achieving state-of-the-art accuracy and efficiency on the MedMNIST v2 dataset, thereby advancing diagnostic precision in 3D medical image analysis. The code is available at https://***/hongkai-wei/MTTM. © 2025 IEEE.

关键词： 3D Medical Image Classification Mamba Token Turing Machine Transformer

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