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Development of an AI based Priority Formation Mechanism in Criminal Law 3

Development of an AI based Priority Formation Mechanism in C...

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3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Sharma, Kashish Amburle, Ankita Shelake, Vijay Chauhan, Ashvini Ashok Dept. of Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Fr. Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (纸本)9798331532420

Nowadays, there is a necessity to tackle a number of pending crime cases across different parts of the world through effective scheduling and management. AI and ML can be used in handling unfinalized criminal cases in India can be one of the possible ways of improving the functioning of the criminal justice system in this country. The approach to this project is to create an AI system that will filter through the open cases and then rank them according to complexity, time-sensitivity, prior performances, and location. Since priority in a case is explained through the use of a supervised learning based on selected set, then law enforcement as well as legal personnel are able to shift most of their attention to other significant cases. Despite feature engineering and data preprocessing being in the construction of the system from the data collected from the various sources such as court records. This means that there is than models such as;Random Forest, Apriori and Rule-Based in an effort to establish the most suitable model. Perhaps the application of this AI based mechanism to form priorities could aid India in better organization of resources and with the assistance of backlog decrease – the justice system in India. But as the right touch to measure artificial use is considered, themes of privacy, bias, and fairness must also be addressed appropriately. © 2024 ieee.

关键词： Crime

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Real-time Occupancy Detection: Modified Background Subtraction with Otsu's Dynamic Thresholding Paradigm

Real-time Occupancy Detection: Modified Background Subtracti...

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2024 international conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Yadav, Rishabh Kumari, Monika Delhi Technological University Dept. of Electrical Engineering New Delhi India

ISBN: (纸本)9798350375237

This paper introduces a modified background subtraction algorithm for real-time occupancy detection in both static and dynamic scenarios. The system, validated through rigorous testing with an ESP32CAM-based setup, delivers accurate insights and reduces false positives while accurately identifying true occupancy events, as indicated by an F1 Score of 0.93. The approach offers efficient, real-time occupancy monitoring without complexity, bridging the gap between manual methods and automated solutions. Conventional manual methods are labor-intensive and inefficient, necessitating automated solutions. This research provides a practical, real-time solution for occupancy detection, essential for energy conservation and automation systems. © 2024 ieee.

关键词： computer vision

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Digital Emotions using Sentiment Analysis for Predictive Insights on Customer Recommendations 5

Digital Emotions using Sentiment Analysis for Predictive Ins...

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5th ieee India Council international Subsections conference, INDISCON 2024

作者： Kundra, Harish Vishnu, Chiluveri Vaishnavi, Balpunuri Venkata Kasireddy, Chandrakiran Reddy Akash, Gundapuneni Jindam, Srihitha Guru Nanak Institutions Technical Campus Computer Science and Engineering Hyderabad India Guru Nanak Institutions Technical Campus Computer Science and Engineering - Internet of Things Hyderabad India Guru Nanak Institutions Technical Campus Computer Science and Engineering - Aiml Hyderabad India Guru Nanak Institutions Technical Campus Computer Science and Engineering - Data Science Hyderabad India

ISBN: (纸本)9798350376753

In an era of unprecedented digital communication and information sharing, understanding the sentiment expressed in textual data is of paramount importance. Sentiment analysis, a subfield of natural language processing, has gained significant prominence due to its potential to decipher the emotions, opinions, and attitudes conveyed through words. This research paper embarks on a journey into the realm of sentiment analysis, where we explore the nuanced interplay of words and emotions in the digital landscape. Electronic commerce (e-commerce) becomes one of the subjects most learned in the education and literature fields of the present day. Superior research in the field electronics focuses mainly on developed economies. From the e-commerce view, the experience of customers has shifted from visiting retail shops to online shopping, making the analysis of customer behavior more salient. In this dataset, the review is taken into account in this sentiment analysis, considering, executing, and analyzing effective ways. Most probabilistic models are used in current research ventures, in the area of text classification, and information retrieval, among other contexts. In turn, ecommerce has grown much broader with online shopping, which is a core platform through which shoppers may be found. In the same breath, it is very imperative to forecast the level of transactions that will be facilitated through e-commerce. This paper will focus on identifying broad patterns in the written opinions of the consumers by using Natural Language Processing (NLP) techniques. The objective of this research is to predict whether customers will recommend the product they purchased by using the information in review text. © 2024 ieee.

关键词： Sales

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Design and Research of Cooling System for Valve Based Electronics 11

Design and Research of Cooling System for Valve Based Electr...

引用

11th international Forum on Electrical engineering and automation, IFEEA 2024

作者： Zhang, Min State Grid Corporation of China Beijing100031 China

ISBN: (纸本)9798331516611

The valve base electronics is the core equipment of a high-voltage direct current (HVDC) transmission system, which has the problem of high operating heat. To achieve its good ventilation and cooling function, the functions and hardware configuration of the valve base electronics were first explained. Then, a design method for a heat dissipation system was proposed, including the design of screen cabinet cooling ducts, selection of heat dissipation fans, and design of cooling modules. Finally, building upon the engineering valve-based electronic equipment, the effectiveness of the cooling system design was verified by testing the air volume of the cooling module and the temperature rise of the screen cabinet. Experiments have shown that this scheme can achieve temperature rise control of valve-based electronic equipment cabinets and chassis within 10K, enabling valve base electronics to operate within a suitable temperature range. © 2024 ieee.

关键词： computer resource management

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Handling Imbalanced Datasets in Researcher Scientific Classification Using Oversampling and Undersampling Approaches 11

Handling Imbalanced Datasets in Researcher Scientific Classi...

引用

11th international conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Zulius, Antoni Defit, Sarjon Stiawan, Deris Abdiansah, Abdiansah Samsuryadi, Samsuryadi Rahmi, Nadya Alinda Universitas Bina Insan Lubuklinggau Universitas Sriwijaya Faculty of Engineering Sciences Faculty of Engineering Palembang Indonesia Universitas Putra Indonesia YPTK Department of Information Technology Faculty of Computer Science Padang Indonesia Universitas Sriwijaya Department of Computer Engineering Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Department of Informatic Engineering Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350355314

The challenge of class imbalance in datasets is a prevalent issue in machine learning, often resulting in biased and inaccurate predictions. This study investigates the effectiveness of different data balancing techniques in enhancing classifier accuracy when applied to an imbalanced Indonesian National Scientific Manuscript dataset. Specifically, we examine how various classifiers respond to three widely used data balancing methods: Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling, and Random Over-sampling. We assess the performance of six classifiers-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), and Gradient Boosting (GB)-when these techniques are applied. The analysis reveals diverse outcomes, with some classifiers showing substantial accuracy gains, while others experience moderate or even adverse effects. The findings underscore the importance of selecting an appropriate balancing method based on the dataset's characteristics and the specific classification algorithm employed in the knowledge classification process. © 2024 ieee.

关键词： Adversarial machine learning

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Generalized Zero-Shot Learning via Implicit Attribute Composition

Generalized Zero-Shot Learning via Implicit Attribute Compos...

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2023 ieee international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Lei Liu, Yang Li, Qiang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9798350337020

Zero-shot learning (ZSL) is an important but challenging task in computer vision that aims to identify unseen classes without matching training samples. Current cutting-edge ZSL methods based on locality focus on acquiring the explicit locality of distinguishing characteristics, which could face a lack of adequate supervision at the class attribute level. This paper introduces a novel approach called IAC, which aims to learn Implicit Attribute Composition for ZSL. This method is more comprehensive compared to attribute localization that solely focuses on class-level attribute supervision. IAC utilizes subspace representations that efficiently capture the inherent structure of high-dimensional image features. Then, we learn implicit attribute composition through subspace representation learning. The superiority of the proposed IAC compared to the state-of-the-art is demonstrated through sufficient experiments conducted on three commonly used ZSL datasets, CUB, SUN, and AwA2. © 2023 ieee.

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Electric Field Simulation and Structure Optimization of 12 kV Environmental-Friendly Gas Insulated Ring Network Cabinet 2

Electric Field Simulation and Structure Optimization of 12 k...

引用

2nd ieee international conference on Control, Electronics and computer Technology, ICCECT 2024

作者： Dongyun, Dai Jiahua, Huang Yimin, You School of Electrical Engineering and Automation Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China School of Electrical Engineering and Automation Xiamen Institute University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China

ISBN: (纸本)9798350380958

The environmental-friendly gas-insulated ring network cabinet is referred to as an environment-friendly cabinet. The high-voltage circuit parts of the environment-friendly cabinet are mainly composed of conductors, VCB, and the knife-brake type three-station disconnector. The insulating support parts are composed of insulating board, insulating arms, and so on. The grounding parts consist of a metal shell and grounding contacts. Simulation of the electrostatic field of the environment-friendly cabinet is during the condition of isolation fracture. Simulation results show that there are local high electric fields on the metal surfaces of the support for VCB, moving contacts, and static contacts, which are 20.7SkV /mm, 10.06kV/mm, and 7.41kV/mm, respectively. Adding a thin metal sheet to the support for the VCB or filling the gap with silicone rubber will reduce the electric field to 4.S1kV/mm and 4.1kV/mm, respectively. The metal shielding structures are installed on both sides of the moving contact and the static contact of the disconnector, and the surface electric field intensity is reduced to 5.25kV/mm and 5.18kV/mm respectively on the disconnector during the state of isolation fracture. The optimized environment-friendly cabinet was tested for power-frequency voltage-withstand test and partial discharge. The results showed that the optimized environment-friendly cabinet passed the power-frequency voltage-withstand test and the magnitude of partial discharge was better than the initial scheme, which verified the feasibility of the optimization. © 2024 ieee.

关键词： Silicones

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Utilization of Encoding, Early Stopping, Hyper Parameter Tuning, and Machine Learning Models for Bank Fraud Detection 9

Utilization of Encoding, Early Stopping, Hyper Parameter Tun...

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9th ieee international Women in engineering (WIE) conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Md Aminul Nag, Anindya Chowdhury, Saumik Fahim, SM Firoz Ahmed Ghosh, Arjan Mumtaj, Nahi School of Engineering Computing Mathematics Oxford Brookes University Oxford United Kingdom Computer Science & Engineering Discipline Khulna University Khulna9208 Bangladesh Jashore University of Science & Technology Department of Computer Science & Engineering Jashore7408 Bangladesh American International University Bangladesh Department of Computer Science & Engineering Dhaka Bangladesh BSRM School of Engineering Electrical and Electronics BRAC University Dhaka Bangladesh

ISBN: (纸本)9798350319651

An effective fraud detection system must protect millions of clients for a secure banking system, which can be achieved using machine learning and AI. In this article, authors have applied four supervised machine learning models: k-nearest neighbors (KNN), random forest (RF), decision tree, and logistic regression (LR) algorithm to detect bank fraud for a synthetic dataset having 1,00,000 rows and 32 columns. Adequate preprocessing, decoding, rigorous feature engineering, validation, performance evaluation, and explanation have allowed the readers to understand the whole study. The algorithms' accuracy is similar for label encoding, which is not prescribed. Still, a significant AUC of 98% has been achieved in Gradient Boosting Models. Further application of this study can be done in real-life cases of banks, insurance, and finance institutions. © 2023 ieee.

关键词： Decision trees

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Multi-Object Detection in 3D Point Cloud's Range Image Using Deep-Learning Technique 14

Multi-Object Detection in 3D Point Cloud's Range Image Using...

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14th international conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Jyothsna, D. Chandra, G. Ramesh Department of Computer Science and Engineering Jntuh Hyderabad India Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350344837

In recent years, LIDARs (Light Detection and Ranging) have gained a lot of insight into various fields such as agriculture, astronomy, robotics, autonomous driving, forestry, etc. Point Clouds obtained through LIDAR have gained attention in the field of computer vision, robotics, and autonomous driving. In computer vision, there are several high level tasks namely classification, detection, tracking, segmentation and registration. Object detection in 3D Point Clouds is one of the challenges faced in the field of computer vision, which is one of the high level tasks. Due to the unordered nature of point cloud data, detecting objects by designing deep neural networks is not a straightforward task. This paper aims to provide a solution to detect the objects in a 3D point cloud data by transforming into 2D range image obtained from LIDAR using the improved YOLOV5 (You Only Look Once- Version5) approach by embedding the coordinate attention module. The improved YOLOV5 is used to detect objects in an image and conducted experiments on different sizes of custom 3D point cloud datasets with and without augmentation. The experimental results showed how the precision, recall and mean average precision (mAP) metrics improve as the dataset size increases. Also, the comparative results with YOLOV5 yielded better results on our data. © 2024 ieee.

关键词： Object detection

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How does artificial intelligence and accountability optimises energy management and cost reduction in Nigeria's manufacturing firms?

How does artificial intelligence and accountability optimise...

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2024 international conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinadewo, Israel S. Dagunduro, Muyiwa E. Akinadewo, Jeremiah O. Oluwagbade, Isaiah O. Akpan, James U. Afe Babalola University Ado-Ekiti Department of Accounting Ekiti State Nigeria

ISBN: (纸本)9798350358155

Manufacturing firms face challenges in aligning technological interventions with effective energy management. The study addresses gaps in understanding the nuanced relationships between AI components and energy outcomes and the sustainable energy practices thereof. The study, therefore, examined the impact of Data Analytics (DA), automation (AUT), and the Internet of Things (IoT), with a focus on the moderating influence of Accountability (ACC) in selected Nigerian manufacturing firms on the optimization of energy management and cost reduction. Utilizing a Likert scale questionnaire, data from 84 Accountants from selected manufacturing firms were gathered. Statistical analyses and correlations were applied to interpret the relationships among variables. Findings reveal a strong positive correlation (r = 0.940) between Data Analytics and Energy Management and Cost Reduction (EMCR). Accountability (ACC) significantly moderates the relationship between Data Analytics and EMCR. There is also a positive impact of automation (AUT) on EMCR (0.235, p © 2024 ieee.

关键词： accountability artificial intelligence automation data analytics energy management and cost reduction

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