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Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 1-24页

作者： Nirbhav Malik, Anand Maheshwar Prasad, Mukesh Department of Geography Delhi School of Economics University of Delhi Delhi India Swami Shraddhanand College University of Delhi Delhi India TGT Computer Science Directorate of Education Delhi Government Delhi India Faculty of Engineering and Information Technology School of Computer Science Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

To lessen damages from landslides, the key challenge is to predict the events precisely and accurately. The objective of this study is to assess landslide susceptibility in the study area. To achieve this objective, a detailed landslide inventory has been prepared based on imagery data and frequent field visits of 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. information gain ratio measure is used to eliminate triggering factors with least score. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proven that the decision tree classification model performed well with good accuracy in forecasting landslide susceptibility. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Landslides

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Clean Sustainable Society Using CNN 4th

Clean Sustainable Society Using CNN

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Sharma, Shivani Goel, Taniya Piplani, Radhika Rai, Bipin Kumar Department of Information Technology ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (纸本)9789819994410

Waste disposal is a significant challenge in India. The rapid increase in the global human population is leading to a surge in mass production. The prevailing model of mass production and disposal follows a linear approach, wherein waste is primarily lost and not recycled or reused. This not only exacerbates environmental problems by increasing pollution but also results in economic losses due to the wasted potential of these materials. This paper basically focuses on an intelligent waste management system for redirecting the various kinds of trash materials to their correct positions by uploading the image of waste in the web application and it will classify images into following categories: reuse, recyclable, and organic waste. To identify the category of waste items, a machine learning model is used, followed by the implementation of a Convolutional Neural Network model to direct the item to the appropriate trash bin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Book Genre Classification system Through Supervised Learning Technique 1

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Rout, Ajit Kumar Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Odisha Gunupur India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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Development and Investigation of Vision system for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Outage Prediction and Improvement in 6G for UAV Swarm Relays Using Machine Learning

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Progress In Electromagnetics Research B 2024年 107卷 33-45页

作者： Khalil, Hisham Ali, Gauhar Rahman, Saeed Ur Asim, Muhammad Affendi, Mohammed El Department of Intelligent Systems Faculty of Information Technology University of Lahore Pakistan EIAS Data Science and Block chain Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia School of Electronic Engineering Xidian University Xi’an China

The issue of signal outages in sub-THz frequency communication for future 6G networks is addressed by this research. A machine learning method is proposed, employing Random Forest and K-Means algorithms to predict the optimal frequency band and outage probabilities for UAV relays. Both space and frequency diversity are explored to enhance signal strength, and metasurface-carrying UAVs are introduced with a 16 × 16 mm2 design. This design significantly reduces the predicted outage probability from 0.1% to 0.0178%. Finally, triangular and hexagonal UAV swarm formations with metasurfaces are investigated, demonstrating improved performance through heatmap results. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Car Lock Control system Using Radio Frequency Identification(RFID)

Car Lock Control System Using Radio Frequency Identification...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Izang Aaron, A. Jonathan, Erihri O. Oyerinde Emmanuel, I. Ominike, Akpovi Chukwu Esther, C. Omodara Oluwademilade, E. Micheal Goldrich, C. School of Computing and Engineering Sciences Babcock University Department of Information Technology Ogun State Ilishan-Remo Nigeria School of Information and Communication Technology Delta State Polytechnic Department of computer Science Ogwashiuku Nigeria Petroleum Training Institute Computer Science and Information Technology Department Delta State Effurun Nigeria School of Computing and Engineering Sciences Babcock University Department of Computer Science Ogun State Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

Car Lock controlled system addresses the critical issue of vehicle security by utilizing RFID technology to create a more secure, passive, and wireless vehicle access system. This innovation surpasses traditional key-based systems, offering enhanced convenience and security. The project involves the development of a digital door locking mechanism integrated with RFID for secure vehicle identification. The system's efficacy was validated through rigorous testing, showcasing its potential to significantly deter vehicle theft. Despite challenges such as signal interference, the outcomes suggest a promising future for RFID in vehicle security. The study concludes with recommendations for extending RF communication range and exploring emerging technologies for further enhancement. This research marks a significant step towards redefining vehicle security, demonstrating the feasibility and effectiveness of a radio frequency-controlled car lock system. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Blockchain-based consensus algorithm for solving security issues in distributed internet of things

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International Journal of Electronic Business 2022年第3期17卷 283-304页

作者： Mohanta, Bhabendu Kumar Samal, Kalpana Jena, Debasish Ramasubbareddy, Somula Karuppiah, Marimuthu Department of Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar751003 India Department of Information Technology VNRVJIET Hyderabad500090 India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad201204 India

In the last decade, internet of things (IoT) emerged as one of the most promising fields of research. The services provided by the IoT have come as a big relief to human life due to the smartness and real-time monitoring without human intervention. IoT applications are widely used as it provides a high level of comfort, automation of the system, and efficiency. To make successful use of the ever-growing IoT applications requires addressing the security, privacy, and trust to protect IoT devices and user privacy from attackers. In this paper, initially different security and privacy challenges are identified in an IoT application. Secondly, analysis regarding blockchain technology is done which gives the idea to solve some of the IoT security challenges. Blockchain-based consensus algorithm PBFT is used to perform secure computation in a smart transportation system. Security analysis shows that the proposed protocol worked correctly in the presence of malicious or faulty nodes. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Internet of things

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Sustainable Drone Surveillance system Using Eternal Vertex Cover and Periodic Charging 17

Sustainable Drone Surveillance System Using Eternal Vertex C...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Dhawde, Navya Chakraborty, Nilotpal Husain Baig, Mirza Galib Anwarul Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Mathematical and Statistical Sciences Mandi India

ISBN: (纸本)9798331531195

Unmanned aerial vehicles (UAVs), or drones, are transforming surveillance strategies in various fields, including border security and wildlife conservation. Their ability to monitor large, challenging areas in real time has established them as a valuable tool, particularly in scenarios where traditional surveillance methods face operational and logistical limitations. This research focuses on optimizing drone-based surveillance along the International borders, and in wildlife conservation areas, where challenges like illegal crossings, cattle smuggling, and poaching present serious security and economic threats. Leveraging UAVs provides a cost-effective, flexible, and highly deployable solution that overcomes the limitations of static surveillance systems and limited human patrols. We propose an optimized drone-based system designed to enhance operational efficiency, coverage, and adaptability in these complex environments, with potential applications in various sectors requiring comprehensive, real-time surveillance. We propose modeling the problem using the Eternal Vertex Cover problem, a dynamic extension of the classical Vertex Cover problem. Given the NP-hard nature of determining this cover number, our research explores heuristic and algorithmic approaches to achieve an efficient and scalable solution. We also propose an intelligent charging mechanism for the drones for their continuous operation. All proposed approaches are evaluated using randomly generated datasets as long as benchmark datasets. © 2025 IEEE.

关键词： Drones

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Optimization of COCOMO-II Model Parameters Using Hybrid Algorithm of Cuckoo Search Algorithm and GA 2

Optimization of COCOMO-II Model Parameters Using Hybrid Algo...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abu-Srhan, Alaa Qasem, Mais Haj Aljaidi, Mohammad Samara, Ghassan Alazaidah, Raed Alsarhan, Ayoub The Hashemite University Faculty of Science Department of Basic Science Zarqa13116 Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa13110 Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Department of Information Technology Zarqa13116 Jordan

ISBN: (纸本)9798350373363

In this paper, we propose a classification model for the detection of phishing domains based on classification rules. We formulate these rules based on the results of the analysis and distribute them into three classifiers: the first is related to domain name features, the second is related to SEO information of the domain, and the third is related to security scans by security vendors. Each classifier feds its results to the main classifier, which then evaluates the received results by its rule. We formulate eleven classification rules, which are distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. We retrieved domain names from 222 confirmed phishing URLs, tested them using the suggested model, and obtained a 99.1% accuracy rate. © 2023 IEEE.

关键词： Classification (of information)

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