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International Conference on computer Communication and informatics (ICCCI)

作者： P Priya Ponnusamy C P Shabariram V R Umayal A Susmeta Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Neelambur Coimbatore India

A Binary Search Tree is expanded into a KD Tree to handle the multi-dimensional key searches. A discriminator makes the KD tree different from the BST. This discriminator will take branching decisions at every level based on the key search. It can handle multi-dimensional coordinate object searching. K-d trees enable O(klogn) lookup times for the k nearest points to some point x. This is extremely useful, especially in cases where an O(n) lookup time is intractable already. In this work, a kd tree has been created based on how a celestial body is positioned in relation to the sun. It aims at finding the minimum in a particular dimension, searching for a celestial body and its nearest neighbor.

关键词： Binary search trees Search problems Sun informatics

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Transformations in the Ways of Improving from Agriculture 1.0 to 4.0 5

Transformations in the Ways of Improving from Agriculture 1....

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Aggarwal, Shruti Verma, Amit Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala India University Centre for Research and Development Chandigarh University Mohali Punjab Gharuan140413 India

ISBN: (纸本)9798350398267

Farming is the need and will consistently be. We human get by on food that is either from plant or from creature. Individuals have developed grounds and breed creatures to acquire nourishment for their endurance since old occasions. This training, known as farming, has advanced after a long haul and reformist cycle, going from Agriculture 1.0 to 4.0. As indicated by the UN Food and Agriculture Organization (FAO) 800 million individuals experiencing hunger and around 8% (650 million) of total populace will be as yet undernourished by 2030. Likewise horticulture portion of worldwide GDP has recently contracted 3% just and we should create 70% more food by 2050. Without a doubt, high requests for food from the overall developing populace are affecting the climate and putting numerous weights on horticultural efficiency. The customary methodology of the horticulture is going through a principal change. Throughout the last numerous long stretches of our set of experiences we have built up a few types of farming, 1.0, 2.0 and now moving towards 3.0 and 4.0 in coming many years. Indeed, today these manifestations are being rehearsed in different places on earth. This paper describes that how the transformation from Agriculture 1.0 to Agriculture 4.0 takes place and the changes in the practices of the agriculture from ancient times to present. © 2022 IEEE.

关键词： Farms

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Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling 3rd

Hypercomplex Generative Adversarial Networks for Lightweigh...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Sfikas, Giorgos Retsinas, George Gatos, Basilis Nikou, Christophoros Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Surveying and Geoinformatics Engineering University of West Attica Athens Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (纸本)9783031090363

Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Microstructure Image Classification of Metals Using Texture Features and Machine Learning 3rd

Microstructure Image Classification of Metals Using Texture ...

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3rd International Conference on Soft Computing and its engineering Applications, icSoftComp 2021

作者： Sabnis, Hrishikesh Angel Arul Jothi, J. Deva Prasad, A.M. Department of Mechanical Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai International Academic City Dubai United Arab Emirates Department of Mechanical Engineering Manipal Academy of Higher Education Dubai Campus Dubai International Academic City Dubai United Arab Emirates

ISBN: (纸本)9783031057663

This research was conducted on microstructural images of four different metals such as mild steel, stainless steel, aluminium, and copper. The research aims to classify the microstructure images of the four metals obtained using light-optical microscope using texture features and machine learning techniques. The images were subjected to different steps like image pre-processing, texture feature extraction and classification. Classical machine learning algorithms like Support Vector Machines, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and XGBoost were used. Experimental results show that the XGBoost classifier achieves the highest classification accuracy of 98% using normalized texture features and cross validation using 10-folds. © 2022, Springer Nature Switzerland AG.

关键词： Decision trees

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Systematic Review of GAN for Enhancing Efficiency in AI in Gaming

Systematic Review of GAN for Enhancing Efficiency in AI in G...

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Advances in Computing research on Science engineering and technology (ACROSET), International Conference on

作者： Jasdeep Singh Sardar M. N. Islam Manav Tewatia Deepanshu Garg Neda Fatima School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of Computer Science And Engineering Victoria University Melbourne Australia School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

A thorough analysis of Generative Adversarial Networks (GANs) and how they might be used to improve AI gaming efficiency is presented in this research. Game developers now have never-before-seen possibilities for innovation, immersion, and personalization thanks to GANs, which have become a potent tool for producing realistic data samples across a variety of fields. This review investigates the architecture, training methods, applications, difficulties, and potential future directions of GANs in AI gaming through a thorough analysis of the body of research and publications. The review demonstrates how GANs can transform the game industry by highlighting their many uses in procedural content generation, texture synthesis, character animation, environment design, and content customization. Nevertheless, there are certain difficulties in integrating GANs into AI games, such as computational resources, mode collapse, training instability, and assessment *** well as legal matters. To tackle these obstacles, multidisciplinary research endeavors and cooperation across academic institutions, corporate entities, and regulatory agencies are necessary. Going forward, new avenues for study and developments in GAN technology hold great potential for getting over these roadblocks and opening up fresh possibilities in AI gaming. We can create a future where AI powered gaming experiences change the parameters of interactive entertainment and provide players globally with more engaging, varied, and personalized gaming experiences by utilizing the possibilities of GANs and addressing the related obstacles.

关键词： Training Visualization Technological innovation Procedural generation Refining Transforms Propulsion Generative adversarial networks Stakeholders Artificial intelligence

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AMDnet:An academic Misconduct Detection Method for Authors’Behaviors

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computers, Materials & Continua 2022年第6期71卷 5995-6009页

作者： Shihao Zhou Ziyuan Xu JinHan Xingming Sun Yi Cao Nanjing University of Information Science&Technology Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing201144China Nanjing University(Suzhou)High and New Technology Research Institute Suzhou215123China Jiangsu Union Technical Institute Wuxi214145China Department of Electrical and Computer Engineering University of WindsorONN9B 3P4Canada

In recent years,academic misconduct has been frequently exposed by the media,with serious impacts on the academic *** research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a *** the most part,these techniques can only detect cases of textual plagiarism,which means that potential culprits can easily avoid discovery through clever editing and alterations of text *** this paper,we propose an academic misconduct detection method based on scholars’submission *** model can effectively capture the atypical behavioral approach and operation of the *** such,it is able to detect various types of misconduct,thereby improving the accuracy of detection when combined with a text content *** model learns by forming a dual network group that processes text features and user behavior features to detect potential academic ***,the effect of scholars’behavioral features on the model are considered and ***,the Synthetic Minority Oversampling Technique(SMOTE)is applied to address the problem of imbalanced samples of positive and negative classes among contributing ***,the text features of the papers are combined with the scholars’behavioral data to improve recognition *** results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.

关键词： academic misconduct neural network imbalanced dataset

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Leveraging Safe and Secure AI for Predictive Maintenance of Mechanical Devices Using Incremental Learning and Drift Detection

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computers, Materials & Continua 2025年第6期83卷 4979-4998页

作者： Prashanth B.S Manoj Kumar M.V. Nasser Almuraqab Puneetha B.H Department of Information Science and Engineering Nitte Meenakshi Institute of TechnologyVisvesvaraya Technological UniversityBelagavi590018India Department of Information Science and Engineering Nitte Meenakshi Institute of TechnologyBangalore560064India Dubai Business School University of DubaiAcademic CityDubaiP.O.Box 14143United Arab Emirates Department of Computer Science and Business Systems Bapuji Institute of Engineering and TechnologyDavangere577004India

Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical *** of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is *** evolution of the data is termed concept *** research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical *** method proposed here is applicable to allmechanical devices that are susceptible to failure or operational *** proposed method in this paper is equipped with the capacity to detect the drift in data generation and *** proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic *** is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural ***,this resulted in poor sensitivity for the smallest drift in the machine data reported as a *** this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.

关键词： Incremental learning drift detection real-time failure prediction deep neural network proactive machine health monitoring

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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A RAG based Personal Placement Assistant System using Large Language Models for Customized Interview Preparation

A RAG based Personal Placement Assistant System using Large ...

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Data Intelligence and Cognitive informatics (ICDICI), International Conference on

作者： Samay Patel Jeet Patel Dhairya Shah Parth Goel Bankim Patel Computer Science & Engineering Department Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Anand India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

This paper introduces a Personal Placement Assistant (PPA) framework that utilizes advanced Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to automate and personalize job placement preparation. The system integrates Natural Language Processing (NLP) techniques, including text embedding using the all-MiniLM-L6-v2 transformer model and semantic retrieval using ChromaDB for accurate resume analysis and context-aware question generation. The PPA is structured into three core components: the Retriever, using PyMuPDF for resume parsing and recursive text chunking for efficient vector storage and search; the Analyzer, employing the Google Gemini-1.5-flash model for domain extraction and percentage-based content profiling; and the Generator, which produces domain-specific MCQs, coding challenges, and interview questions aligned with Bloom’s Taxonomy. RAG enhances the system’s ability to integrate external knowledge, improving the contextual relevance of the generated content. Evaluation results demonstrate an 83.77% accuracy in domainspecific extraction and question generation, confirming the PPA’s effectiveness in automating personalized job preparation across industries.

关键词： Analytical models Accuracy Large language models Retrieval augmented generation Resumes Transformer cores Transformers Question generation Vectors Interviews

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