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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3832-3844页

作者： Gupta, Prashant K. Sharma, Deepak Andreu-Perez, Javier Bennett University School of Computer Science & Engineering Technology Uttar Pradesh Greater Noida201310 India Christian-Albrechts-University Kiel Department of Computer Science Kiel24118 Germany University of Essex Centre for Computational Intelligence School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom University of Jaen Spain

Scoring the driver's behavior through the analysis of his/her road trip data is an active area of research. However, such systems suffer from a lack of explainability, integration of expert bias in the calculated score, and ignoring the semantic uncertainty of various variables contributing to the score. To overcome these limitations, we have proposed a novel perceptual computing based unsupervised scoring system. The prowess of the proposed system has been exemplified in a case study of driver's scoring from telemetry data. Our proposed approach yields scores that showed a higher significant separability between drivers performing responsible or irresponsible (aggressive or drowsy) driving behaviors, than the formal method of computing these scores (a p value of 3.94×10-4 and 3.42×10-3, respectively, in a Kolmogorov-Smirnov test). Further, the proposed method displayed higher robustness in the bootstrap test (where 30% of original data were omitted at random) by providing scores that were 90% similar to the original ones for all results within a confidence interval of 95%. © 2020 IEEE.

关键词： Fuzzy logic

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Crop Damage Detection using Machine Learning Approaches 6

Crop Damage Detection using Machine Learning Approaches

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Nair, Priyanka C Sampath, Nalini Reddy, P Praneeth Shruthi, P Sai Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350381689

Agriculture is of vital importance to human life as it is the main source of livestock production and contributes significantly to the country's employment opportunities and economy. Ensuring high standards of production quality is essential. Recent technological advances are merging agricultural and machine learning techniques to help improve crop quality. The main objective of this work is to make use of machine learning approaches to identify the healthy status of the crop. Multi-class classification is applied to the dataset which helps classify crop conditions. As there is an imbalance of data of the 3 classes, Random under- sampling and Tomek under-sampling methods are applied on the dataset. A combination of Tomek undersampling and Smote undersampling has also been performed on this dataset. Various algorithms like Gradient Boosting, Gaussian Naïve Bayes, Neural Network (MLP) and k-Nearest Neighbors were performed on the dataset which helped show that Randomforest was the best model in terms of accuracy and F1 score. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Steel Defect Detection using U-Net Model with Attention Mechanism 5

Enhanced Steel Defect Detection using U-Net Model with Atten...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sai, D. Gowtham Sri Mani Maheswari, Uma Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In this research, detection of steel defects is a significant use of computer vision that can enhance industrial quality control. New prospects for automated defect segmentation are presented by recent developments in deep learning. This paper investigated the use of attention processes in a U-Net convolutional neural network architecture for improved steel flaw identification. Images of steel and matching defect masks from the Severstal Steel Defect Detection dataset were used to train the U-Net network. Preprocessing actions such as image rotation and scaling were performed in addition to data augmentation. When tweaked at various probability thresholds, the model produced defect mask predictions on a test set with good accuracy. When compared to ground truth masks, qualitative results showed that different defect types could be segmented well. Forecasts on fresh test pictures without any masks provided also shown a believable ability to identify flaws. According to this research, deep learning using attention blocks and U-Nets has the potential to improve automation in tasks related to semantic segmentation, such as steel inspection. To assess the model's efficacy on other defect types and steel materials, more work is required. The code and trained model serve as a basis for creating systems that are ready for production. © 2024 IEEE.

关键词： Attention mechanisms Convolutional neural networks Image processing Steel defect detection U-Net

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Beyond Search: A Network-Based Exploration of Buyer Behavior in the OLX Car Market 4

Beyond Search: A Network-Based Exploration of Buyer Behavior...

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4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Ashvath, Y. Moin Ramya, G.R. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375466

The information on used cars offers a thorough analysis of the second-hand car industry. This dataset may be utilized to construct predictive models that can estimate the price of a used car based on its characteristics, including the make, model, year, mileage, fuel type, and condition. This dataset enables comprehensive data analysis to extract insights into market trends, pricing dynamics, and buyer-seller behavior. The main goals are to examine price patterns depending on different characteristics, assess the influence of fuel type and geographical factors on pricing and demand, investigate the correlation between mileage and resale value, and determine the most popular automobile models. Furthermore, the dataset enables the development of predictive models that can estimate both selling prices and the amount of time it takes to sell a product. Methodologies encompass the processes of data cleaning and exploratory data analysis (EDA). The results will assist in identifying communities or clusters within the dataset, uncovering groups of cars with similar attributes or market characteristics. The results aim to offer comprehensive market insights, improve comprehension of buyer behavior, and create predictive tools to assist sellers, buyers, and platform operators in decision-making. Ultimately, this will optimize strategies and operations in the used car market. © 2024 IEEE.

关键词： Sales

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Blockchain-based Decentralized Digital Forensics Case Management System using IPFS 3

Blockchain-based Decentralized Digital Forensics Case Manage...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Viswanathan, Jayaraj Kumar, S. Udhaya Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354348

Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance. © 2024 IEEE.

关键词： Data integrity

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An Efficient Approach to Data Clustering Using the K-Means Algorithm in Big Data Analytics 3

An Efficient Approach to Data Clustering Using the K-Means A...

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

作者： Vani, S. Sandhiya Ramya, P. Nagendar, Y. Prakash, P. Arun Chanti, Malladi Atheeswaran, Athiraja Rajalakshmi Engineering College Department Of Computer Science and Engineering Thandalam Chennai India Anna University University College of Engineering Department of Computer Science and Engineering Dindigul India Sr University Department of School of Computer Science and Artificial Intelligence Warangal Telangana Hyderabad India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Maisammaguda Telangana Hyderabad India Telangana Hyderabad India

ISBN: (纸本)9798331515911

The large-scale, high-dimensionality of datasets poses major obstacles to effective and precise data clustering in the field of big data analytics. This work focuses on a well-known technique for dividing huge datasets into informative clusters: the K-Means algorithm. This approach to data clustering is efficient. However, because of scalability and computational complexity problems, K-Means is not as effective when managing large amounts of data. In order to reduce processing cost and improve clustering accuracy, this work provides an improved version of the K-Means algorithm that blends dimensionality reduction approaches and efficient initialization methods. Principal Component Analysis (PCA) is used as a preprocessing step in the methodology to reduce the dimensionality of huge datasets. An optimized K-Means algorithm with improved centroid initialization is then applied. These changes lead to shorter iteration durations and higher-quality clusters. Comparing the results to standard K-Means, experiments on many big data benchmarks showed a 15% gain in cluster accuracy and a 25% reduction in execution time. To sum up, the incorporation of optimized initialization techniques and dimensionality reduction greatly improves the scalability and performance of K-Means in big data analytics, with useful implications for sectors that depend on effective data analysis. © 2025 IEEE.

关键词： Data accuracy

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Deep Learning for Brain Stroke Disease Management: A Mapping Study

Deep Learning for Brain Stroke Disease Management: A Mapping...

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International Symposium on Automatic Control and Emerging Technologies, ACET 2023

作者： Barouhou, Annas Benhlima, Laila Bah, Slimane Elmaghraoui, Hajar Jaggour, Imane Belmoudden, Oumaima Computer Science Department Mohammadia School of Engineering Rabat Morocco Computer Science Department Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9789819701254

The origin of the word "stroke" is based on the concept of receiving a sudden blow or impact, which reflects how quickly the symptoms of this illness can appear - people can be suddenly struck by it. A stroke occurs when the supply of blood to the brain is interrupted, leading to damage in brain cells due to a lack of oxygen. The resulting set of symptoms is referred to as a stroke. Every year more than 15 million people worldwide are affected by stroke. Of these, 7 million die, and the other five million are left disabled. Since the brain governs the entire body, the symptoms of a stroke can vary extensively, contingent on the specific areas of the brain that are impacted. Recently, deep learning has emerged as a powerful tool in the healthcare field, allowing stroke patients to receive tailored and efficient clinical care. This paper aims to assess the role of various deep learning approaches utilized for brain stroke disease management using a systematic mapping methodology. Over the period from 2010 to 2022, a total of 1652 studies were unearthed by querying six widely used digital libraries. To the best of our knowledge, no formal mapping study has been conducted on the application of deep learning on brain stroke management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Diseases

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Energy Efficient Dual User Association for Large Scale IRS-Aided mmWave Communication Networks

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China Communications 2025年第1期22卷 182-195页

作者： Chen Guolin Deng Yiqin Huang Xiaoxia Fang Yuguang School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Control Science and Engineering Shandong UniversityJinan 250100China Department of Computer Science City University of Hong KongHong Kong 999077China

The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention *** the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS ***,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and *** problem is efficiently addressed in two ***,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE *** reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and *** results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

关键词： energy efficiency intelligent reflecting surface,millimeter-wave communication

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Voice assistance to blind people by detection of surrounding objects, activities, and humans

Voice assistance to blind people by detection of surrounding...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Kambham, Suhas Preetham Varman, Raahul Ramya, Tadepalli Sri Valli Eniyan, M.K. Tangavel, Senthil Kaumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Mathematics and Computer Science Ashland University United States

ISBN: (纸本)9798331510299

Various systems were developed to assist those with vision impairments and enhance their standard of life. Regrettably, the majority of these systems have restricted functionality. We decided to build a model that detects all possible surrounding things and guides them through voice description. Our main objective is to detect the obstacles in the person's pathway and warn them using voice description [1]. The model can assist blind people from the surrounding obstacles which exist in their pathway. The primary goal of the model is to identify the nearby obstacles - vehicles, animals, persons, objects, etc, in the person's pathway and guide or warn them using voice description. Our proposed system integrates advanced real-time obstacle detection and voice-guided navigation using a novel combination of DepthNet module with convolutional LSTM layers, enhancing spatial context awareness compared to traditional models. This approach, along with fine-tuning on diverse datasets such as MS COCO and BDD100K, ensures robust performance in varied environments and conditions. The real-time guidance capability is further improved by the integration of optical flow algorithms, which accurately estimate motion in dynamic scenes. This comprehensive solution significantly enhances the assistive technology for those with visual impairments. © 2024 IEEE.

关键词： Obstacle detectors

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MAESTRO-EP: Multi-Architecture Ensemble System for Temporal Reasoning and Outcome Prediction in Event Processing 8

MAESTRO-EP: Multi-Architecture Ensemble System for Temporal ...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Shekar, Vaibhav Devaraja, G. Mahadevan, Anbazhagan Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331531539

MAESTRO-EP (Multi-Architecture Ensemble System for Temporal Reasoning and Outcome Prediction in Event Processing) is an innovative deep learning framework designed to model and predict outcomes in complex event-driven systems. This paper introduces a novel multi-head ensemble architecture that leverages diverse neural network models, including RNNs, LSTMs, CNNs, Transformers, and GRUs, to capture intricate temporal patterns in event sequences. Our approach utilizes a minimal event schema, allowing the model to organically learn complex behaviors across various domains, from supply chain networks to game environments. We evaluate MAESTRO-EP on multiple datasets, including supply chain simulations, Tic-Tac-Toe, and pathfinding algorithms, demonstrating its superior performance in context output accuracy. The system's highly scalable and adaptable architecture provides a robust framework for future research in adaptive AI systems and digital twin simulations, with the potential to significantly advance event sequence modeling capabilities in dynamic multi-agent environments, particularly in complex supply chain networks. © 2024 IEEE.

关键词： Multi agent systems

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