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Exploratory Data Analysis of Titanic Survival Prediction using Machine Learning Techniques 2

Exploratory Data Analysis of Titanic Survival Prediction usi...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Gupta, Anshika Arora, Deepak Tiwari, Shivam Aset Lucknow Amity University Dept. of Computer Science & Engineering Uttar Pradesh India Gl Bajaj Institute Technology & Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9781665456302

It is very important to find out the root causes of human tragedies in the past so that future crises can be avoided. The incident of 15 April 1912 is an example of a human tragedy in which around 1500 passengers and working staff lost their lives. Continuous research in today's time shows that if the proper statistical assessment was done then it would probably be possible that human devastation could be reduced. In today's era, many new and powerful technologies are available, with the help of which accurate statistical calculations can be done. In this research study, Titanic survivals have been studied based on machine learning techniques. In the study, out of the total entities, 891 entities have been used for training and 418 entities have been used for the test set and the comparative study of different machine learning algorithms gives importance to this research study. © 2023 IEEE.

关键词： Machine learning

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Image Processing based Skin Cancer Recognition using Machine Learning 3

Image Processing based Skin Cancer Recognition using Machine...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Singh, Sanjay Pilania, Urmila Kumar, Manoj Awasthi, Satya Prakash Manav Rachna University Dept. of Computer Science & Technology Faridabad India Poornima University Faculty of Computer Engineering Jaipur India

ISBN: (纸本)9798350338607

The skin is the most important part of the human body because it protects bones and muscles., hence, it is the body's largest organ. Numerous people are suffering from skin cancer these days. It has now become the most common type of cancer. Melanoma is known to be a serious and mortal disease. If detected in initial stages, it can be easily treated. Non-melanoma skin cancers have also become common. Rrecognizing skin cancer automatically from the images can help in faster diagnosis and enhanced accuracy saving valuable time. In this paper, 4 types of CNN were used named VGG16, InceptionV3, ResNet152V2 and a special CNN with 12 layers. © 2023 IEEE.

关键词： Machine learning

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Enhancing Telecommunications Customer Retention: A Deep Learning Approach Using LSTM for Predictive Churn Analysis 2

Enhancing Telecommunications Customer Retention: A Deep Lear...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Kumar, M. Ranjith Priyanga, S. Anusha, Janani Srinivasan Chatiyode, Veda Santiago, Jerome Chaudhary, Dharminder School of Computer Science and Engineering Vellore Institute of Technology Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engg Chennai India

ISBN: (纸本)9798350373110

The telecommunications industry has a significant challenge in customer churn, especially for companies aiming for sustainable growth in a competitive market. To overcome this issue, we suggested a method utilizing Long Short-Term Memory (LSTM) networks, a deep learning strategy that has revolutionized predictive modeling. We used 4250 customer profiles, each containing 19 features including consumption data and plan options, to train and test the LSTM model. Data preprocessing included encoding categorical variables, normalizing numerical features, and eliminating irrelevant variables to maintain data integrity and authenticity. The LSTM model was fine-tuned to recognize significant patterns rather than simply memorizing data. We also incorporated an additional layer to assess diverse information types, which improved the model's ability to process sequential data effectively. The proposed model achieved a prediction accuracy of 95.3% in identifying potential customer churn, demonstrating its effectiveness in predicting when customers might leave their telecommunications provider. These results demonstrate how LSTM networks can reduce customer attrition in the telecom industry, making them a valuable tool for companies aiming to retain customers and maintain growth in a competitive market. The study's findings pave the way for innovative applications of deep learning techniques in customer retention strategies across diverse industries. This breakthrough has the potential to transform business approaches to customer relationship management and loyalty programs. © 2024 IEEE.

关键词： Commerce

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Precision Agriculture: Crop Image Segmentation and Loss Evaluation through Drone Surveillance 3

Precision Agriculture: Crop Image Segmentation and Loss Eval...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Patidar, Pawan Kumar Tomar, Deepak Singh Pateriya, R.K. Sharma, Yogesh Kumar Maulana Azad National Institute of Technology Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350300710

Agriculture is the most important source of livelihood. Crop segmentation has become an important role in precision agriculture which helps farmers to make decisions about crop damage and its production. However, it's a challenging task to achieve precision in the agriculture field. Drone Surveillance helps to achieve that crop yield assessment, crop damage, crop health, and other parameters. This paper focuses on image segmentation of crops, classified into categories like sparse and dense crops with the multitemporal data image taken by Drone. This model proposed and studied shows the loss percentage in crop identification by image segmentation process, it helps farmers to get good compensation for crops to survey through Drone (UAV) techniques. A detailed analysis with outcome of thisis explained further. © 2023 IEEE.

关键词： Drones

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Design of an Efficient Model for Wheat Crop Disease Detection Using Multimodal Fusion and Uncertainty Quantification 1

Design of an Efficient Model for Wheat Crop Disease Detectio...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Prasad, Sadanand Chaudhary, Sumit Das, Purushottam Kumar, Punit Kirola, Madhu Anandaram, Harishchander Uttaranchal University Uttaranchal Institute of Technology Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Jb Institute of Technology Dept. of Mechanical Engineering Dehradun India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India

ISBN: (纸本)9798350392111

The basic requirement for precise and proficient yield sickness discovery in wheat development has prodded progressions in rural innovation. Notwithstanding progress, existing strategies frequently miss the mark in completely coordinating assorted information modalities and tending to vulnerabilities inborn in expectation models. This study presents a novel strategy for wheat crop disease detection that overcomes these limitations using novel methods. First and foremost, the constraints of current techniques are outlined, underscoring their deficiency in utilizing multimodal information actually and evaluating vulnerability powerfully. These weaknesses highlight the need for novel philosophies able to synergistically incorporate assorted information sources and give dependable vulnerability gauges. A multifaceted framework comprising Graph Neural Networks (GNNs), Transfer Learning with Attention Mechanisms (TLAM), Bayesian Neural Networks (BNNs), and Graph-based Convolution Networks (GCNs) is what we propose as a solution to these issues. Every strategy is fastidiously picked for its capacity to bridle the intricacy of horticultural information, model complicated connections, and evaluate vulnerability, in this way upgrading the exactness and interpretability of illness location. The proposed approach displays a few benefits, including further developed infection location precision through multimodal combination, upgraded interpretability using consideration systems, vigorous vulnerability measurement utilizing Bayesian methods, and exact spatial-worldly investigation worked with by chart-based convolutions. These headways increase the viability of illness location as well as enable partners with noteworthy experiences for proactive sickness the executives and yield streamlining. © 2024 IEEE.

关键词： Graph neural networks

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

引用

33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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Predictive Analysis of Jordan Road Accidents Severity Using Machine Learning

Predictive Analysis of Jordan Road Accidents Severity Using ...

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Sawaged, Layan Khabour, Safaa M. The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan

ISBN: (纸本)9798350335590

Jordan has a high and growing level of traffic accidents reaching 160,600) accidents in 2021, (11,241) of them had human injuries, (589) deaths, and (320) million JOD losses. Road traffic accidents are currently the 8th leading cause of death worldwide, accounting for about 1.35 million fatalities annually. Understanding the primary causes of these accidents and the circumstances in which they occur is essential if governments throughout the world are to put policies in place to limit the number of fatalities brought on by traffic accidents. This project has two objectives: (i) to learn more about Jordan's present road accident situation by performing an exploratory study on several road accident datasets, and (ii) to investigate the effectiveness of various machine-learning techniques in predicting the severity of road accidents in Jordan. The dataset entities were collected, collated, explored, and prepared for use in the model. To assess their predictive performance, five different classification algorithms were trained and tested. The findings of the present study demonstrate that the best algorithm was Logistic Regression, which had an accuracy of 98.1%. © 2023 IEEE.

关键词： Machine learning

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Exploring the Integration of Generative AI in Modern Education Systems: A Comprehensive Analysis

Exploring the Integration of Generative AI in Modern Educati...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dey, Avick Kumar Chauhan, Vineet Kumar Singh, Pradeep Kumar Sarkar, Ananya Dept. of Computer Applications West Bengal Durgapur India Galgotias College of Engg. & Tech Dept. of Information Technology Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India DSMS College of Tourism and Management Dept. of Computer Applications West Bengal India

ISBN: (纸本)9798331517953

This paper aims at filling this gap through dissecting the applicability of the Generative AI in the current learning and teaching system. The paper offers an introduction to Generative AI technologies: GPT and DALL-E, and describes the chronological history and current progress of its educational applications. Other objectives include discussing about the uses of Generative AI in adaptive learning environment, smart learning systems, automation of organizational work and in talented minds improvement. Analyzing key successful AI cases, the paper reveals the challenges implementers encountered, and provides debates about the appropriate practices of AI. Research evidence points to the prospect of Generative AI benefiting students and educators in teaching and learning, which indicates the innovative potential of the technology, yet at the same time reveals the potential risks and challenges that need to be addressed concerning the ethical and privacy implications of Generative AI in education. Others may follow general recommendations for policymakers and educators on how to integrate artificial intelligence responsibly, as well as future work for this quickly burgeoning domain. © 2024 IEEE.

关键词： Contrastive Learning

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Design and Characterization of a Ka-Band Parasitic Patch Antenna for 5G Applications 9

Design and Characterization of a Ka-Band Parasitic Patch Ant...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kouser, Kahkashan Hannan, Mohammad Shafi, Faizan Aknan, Mohammad Dept. of Computer Science & Engineering Bihar Motihari India Rajiv Gandhi Institute of Petroleum Technology Dept. of Electrical and Electronics Engineering Uttar Pradesh Amethi India Indian Institute of Technology Dept. of Electrical Electronics and Communication Engineering Dharwad India

ISBN: (纸本)9798350377972

He design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable for compact wireless *** design of parasitic patch antennas for high-frequency applications, such as 5G millimeter-wave (mm-wave) communication, presents several challenges, including achieving high gain, wide bandwidth, and efficient impedance matching within a compact structure. Recent techniques, such as the use of parasitic elements and stepped impedance feedlines, have shown promise in enhancing antenna performance. However, these methods still face limitations related to fabrication complexity, signal degradation, and reliability under varying conditions. This work introduces a circular patch antenna with a parasitic reflector element specifically designed for Ka-band applications at 28 GHz. The proposed design addresses these challenges by optimizing the placement of parasitic elements to achieve high gain, a wide bandwidth of 2 GHz, and stable VSWR performance across the operating range. The single-layer, single-side etching structure further simplifies fabrication, making it suitable

关键词： Slot antennas

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