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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Samikshya Dash Simanchala Das S. Sivasubramanian N. Kalyana Sundaram Harsha K G T. Sathish School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India KL Business School Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science JSS Academy of Technical Education Noida Uttar Pradesh India Department of Mechanical Engineering Saveetha School of Engineering Chennai Tamil Nadu India

Safeguarding financial institutions and their consumers against fraudulent activity makes fraud detection a top priority in the banking and finance business. There has been a rise in the development of artificial intelligence-based fraud detection systems in tandem with the popularity of machine learning methods. This study presents a comprehensive evaluation of modern machine learning approaches like neural networks in comparison to more conventional ones like logistic regression and decision trees. These techniques are tested using financial and banking data from the real world, and the findings indicate that neural networks are superior to more conventional approaches. In addition, our research emphasizes the significance of data gathering and administration in the evolution of fraud detection systems.

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A Dual-Stage Deep Learning Model for Cyber Data Classification and Analysis

A Dual-Stage Deep Learning Model for Cyber Data Classificati...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Sarangapani Nivarthi D.S. Wankhede D. Anandhasilambarasa N Wamika Goyal Srividhya S Diksha Aggarwal School of Management - PG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Cyberbullying is on the rise, according to recent research, especially among adolescents. In this research, we provide an innovative approach to text-based automated categorization for the purpose of detecting cyberbullying. However, due to the problem of over fitting, a classifier cannot offer a locally converging solution. Given these constraints, we built a text categorization engine to do some preliminary work on the tweets, erasing noise and background details before extracting the required features and categorising without data overfitting. In this research, we create a new Dual Stage Deep Learning (DSDL) method by first using Deep Neural Networks for feature engineering and then employing Deep Q-Learning for classification. Results from validating the innovative Deep classifier show that it increases text categorization accuracy.

关键词： Deep learning Analytical models Accuracy Q-learning Text categorization Cyberbullying Psychology Feature extraction Data models Overfitting

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Trap Characterization of InGaN/GaN Blue Light Emitting Diode Grown on Si Substrate

Trap Characterization of InGaN/GaN Blue Light Emitting Diode...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Chen, Jiaxiang Qu, Haolan Qi, Longheng Liu, Yaying Zhang, Xu Lau, Kei May Zou, Xinbo School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai200031 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Three hole traps and one electron trap were revealed for InGaN/GaN blue LEDs grown on Si by a combination of DLTS and ICTS. Time-resolved thermal-enhanced emission process of deep hole trap was investigated. © Op... 详细信息

ISBN: (纸本)9781957171005

关键词： III-V semiconductors

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Precise Interception Flight Targets by Image-based Visual Servoing of Multicopter

arXiv

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arXiv 2024年

作者： Yan, Hailong Yang, Kun Cheng, Yixiao Wang, Zihao Li, Dawei The School of Computer Science Northwestern Polytechnical University Xi’an710129 China The School of Automation Science and Electrical Engineering The School of Future Aerospace Technology The Institute of Unmanned Systems Respectively Beihang University Beijing100191 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

nterception of low-altitude intruding targets with low-cost drones equipped strapdown camera presents a competitive option. However, the malicious maneuvers by the non-cooperative target and the coupling of the camera make the task challenging. To solve this problem, an Image-Based Visual Servoing (IBVS) control algorithm based on proportional navigation guidance with field-of-view holding capability is designed. The proposed controller reduces the miss distance while improving the stability of the visual servo system during interception. Software-in-the-loop (SITL) simulation experiments show a 72.8% reduction in the circular error probability (CEP) compared to the most recent study. This improvement enhances interception accuracy from the decimeter to the centimeter level. Real-world experiments further validate the effectiveness of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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MULTI-OBJECTIVE GREEN MIXED VEHICLE ROUTING PROBLEM UNDER ROUGH ENVIRONMENT

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Transport 2021年第1期37卷 51-63页

作者： Dutta, Joydeep Barma, Partha Sarathi Mukherjee, Anupam Kar, Samarjit De, Tanmay Pamučar, Dragan Šukevičius, Šarūnas Garbinčius, Giedrius Dept of Computer Science and Engineering National Institute of Technology Durgapur West Bengal India Dept of Mathematics National Institute of Technology Durgapur West Bengal India Dept of Logistics University of Defence in Belgrade Military Academy Belgrade Serbia Dept of Mobile Machinery and Railway Transport Vilnius Gediminas Technical University Vilnius Lithuania Dept of Automobile Engineering Vilnius Gediminas Technical University Vilnius Lithuania

This paper proposes a multi-objective Green Vehicle Routing Problem (G-VRP) considering two types of vehicles likely company-owned vehicle and third-party logistics in the imprecise environment. Focusing only on one objective, especially the distance in the VRP is not always right in the sustainability point of view. Here we present a bi-objective model for the G-VRP that can address the issue of the emission of GreenHouse Gases (GHGs). We also consider the demand as a rough variable. This paper uses the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed model. Finally, it uses Multicriteria Optimization and Compromise Solution (abbreviation in Serbian – VIKOR) method to determine the best alternative from the Pareto front. © 2021 The Author(s). Published by Vilnius Gediminas Technical University.

关键词： Vehicle routing

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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Unsupervised Hashing with Semantic Concept Mining

arXiv

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arXiv 2022年

作者： Tu, Rong-Cheng Mao, Xian-Ling Lin, Kevin Qinghong Cai, Chengfei Qin, Weize Wang, Hongfa Wei, Wei Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China Beijing Institute of Technology Southeast Academy of Information Technology Fujian China National University of Singapore Singapore School of Computer Science Zhejiang University Zhejiang China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science Huazhong University of Science and Technology Wuhan China

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task. The source codes are available 1 Copyright © 2022, The Authors. All rights reserved.

关键词： Image retrieval

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Leveraging knowledge of Previous Baskets to Anticipate online Buyer Behaviour

Leveraging knowledge of Previous Baskets to Anticipate onlin...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Priyank Sirohi Niraj Singhal Pradeep Kumar Mahboob Alam Shobhit Institute of Engineering and Technology (Deemed-to-be University) Meerut India Sir Chhotu Ram Institute of Engineering and Technology Chaudhary Charan Singh University Meerut India Department of Computer Science and Engineering JSS Academy of Technical Education Noida Uttar Pradesh India Manav Rachna International Institute of Research and Studies Faridabad

Customer behavior prediction is an important task for any company to improve the sale of product. Retailer used Market Basket Analysis pattern of customer to identify the behavior of customers. On the basis of previous basket analysis one most important feature suggestion for customer for next purchasing. Market Basket Analysis was successfully applied to evaluate a large amount of data to understand customer behavior of purchasing patterns. To facilitate reordering and maintaining adequate product stock, this paper will explain how Instacart can use its consumer transaction history and concentrate on descriptive analysis of customer buying habits, objects frequently purchased around each other, and units purchased from of the shop. Additionally, to locate client subsets and cluster with similar purchasing habits and to visualize the data to make useful recommendations geared toward enhancing revenue and satisfaction using segment and forecasting framework. In this paper design a machine learning framework to forecast whichever previously purchased item will be in the customer future purchasing. Proposed model has high accuracy on the same data set is 74.16 percent.

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A Novel Blockchain-Integrated Distributed Data Storage Model with Built-in Load Balancing

A Novel Blockchain-Integrated Distributed Data Storage Model...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Obadah Hammoud Ivan A. Tarkhanov Department of Engineering Cybernetics National University of Science and Technology Moscow Russia Federal Research Center “Computer Science and Control” of Russian Academy of Sciences State Academic University for Humanities

ISBN: (纸本)9781665451635

Most companies relay on centralized servers, which are considered as a fast and easy to deploy solution for file sharing, but they have many downsides, like security and trust issues, which can be solved using DLT (blockchain) solutions. However, DLT-based solutions have many challenges, such as nodes orchestration, adjusting reliability and double-spending, as well as load balancing. This research proposes a DLT (Distributed Ledger technology) based model for files storage for companies using p2p networks, which takes into account both data reliability and storage space efficiency. More efficient data storage is achieved through the use of the Erasure coding technique. For load balancing, a set of algorithms that can be implemented in the form of a smart contract were proposed. The described model can become the basis of the IT infrastructure for trusted exchange of documents, technical and scientific information, games distributions, mobile applications, updates of scientific, regulatory and technical databases in b2b and b2c format

关键词： Distributed ledger Smart contracts Memory Load management Data models Encoding Blockchains

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AI-Driven Precision Agriculture: Techniques for Monitoring Crop Health and Yield Optimization

AI-Driven Precision Agriculture: Techniques for Monitoring C...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Deepak Sharma M. Chitra Devi Vivek Veeraiah Manisha Kasar Deepshikha Aggarwal Tripti Sharma Jagannath University Jaipur Rajasthan India Dept. of Computer Science SRM Trichy Arts and Science College Tamil Nadu India Dept. of Computer Science Sri Siddhartha Academy of Higher Education Karnataka India Dept. ofCSE Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune Maharashtra India Dept. of IT Jagan Institute of Management Studies Delhi India Dept. of CSE Rungta College of Engineering & Technology Bhilai Chhattisgarh India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

AI-driven precision agriculture is revolutionising the agricultural business by using advanced techniques to monitor crop health and optimize output. By integrating data from sensors, drones, and satellite photos with artificial intelligence, farmers may get up-to-date information on crop condition, soil health, and environmental factors. Through meticulous analysis of vast amounts of data, these artificial intelligence systems are capable of detecting the first signs of pests, diseases, and nutritional deficiencies. This enables timely interventions to enhance the overall health of crops. AI-driven predictive analytics manage the timing of irrigation, fertilisation, and harvesting to enhance productivity and guarantee optimum resource use. This approach addresses growing global need in case of food while also improving agricultural sustain ability and productivity. The Python script demonstrates integration of AI and IoT technologies into a Precision Agriculture System to optimize agricultural yields and monitor crop health. It generates a synthetic dataset with real-world parameters and evaluates the Random Forest Regressor model on training along with testing data. The simulation provides visual insights and performance metrics, highlighting the potential of AI and IoT in precision agriculture in case of enhanced decision-making along with productivity.

关键词： Precision agriculture Productivity Temperature sensors Temperature measurement Visualization Decision making Crops Artificial intelligence Monitoring Testing

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