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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Sriprasadh K Shamik Palit B Pravallika Manjunatha Reena Lenka Atul Singla Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Vadapalani Chennai Department of Computing Science and Software Engineering RAK Campus University of Stirling UAE Institute of Aeronautical Engineering Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Symbiosis Institute of Management Studies Symbiosis International (Deemed) University Pune Lovely Professional University Phagwara India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques.

关键词： Consumer behavior Social networking (online) Machine learning Companies Market research Real-time systems Libraries

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A Review of Dark Web: Trends and Future Directions

A Review of Dark Web: Trends and Future Directions

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Shahriar Sobhan Timothy Williams Md Jobair Hossain Faruk Juan Rodriguez Masrura Tasnim Edwin Mathew Jack Wright Hossain Shahriar Institute for Cybersecurity Workforce Development Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department of Computer Science Kennesaw State University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The dark web is often discussed in taboo by many who are unfamiliar with the subject. However, this paper takes a dive into the skeleton of what constructs the dark web by compiling the research of published essays. The Onion Router (TOR) and other discussed browsers are specialized web browsers that provide anonymity by going through multiple servers and encrypted networks between the host and client, hiding the IP address of both ends. This provides difficulty in terms of controlling or monitoring the dark web, leading to its popularity in criminal underworlds. In this work, we provide an overview of data mining and penetration testing tools that are being widely used to crawl and collect data. We compare the tools to provide strengths and weaknesses of the tools while providing challenges of harnessing massive data from dark web using crawlers and penetration testing tools including machine learning (ML) techniques. Despite the effort to crawl dark web has progressed, there are still rooms to advance existing approaches to combat the ever-changing landscape of the dark web.

关键词： Crawlers Machine learning software Browsers Data mining Servers IP networks

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Evaluating Actors' Performance Using Machine Learning for Gesture Classification

Evaluating Actors' Performance Using Machine Learning for Ge...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hossam Ahmed Omar Amr Ayman Atia Software Engineering Department October University for Modern Sciences and Arts (MSA) Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Helwan University Giza Egypt Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

In the context of advancing technologies in the entertainment industry, this study explores the utilization of machine learning (ML) for assessing actors' performances. The integration of technology in that industry is limited. The methodology encompasses acquiring performance ratings from the model to recognize predefined criteria. These ratings are then subjected to comparative analysis to gauge the system's effectiveness. The system showed 83.2% accuracy using MediaPipe to extract the positions of body part points for each gesture. Then, using One Dollar Py library, positive, mediocre, and negative models are developed using the patterns extracted from MediaPipe. We benchmarked the system against an assessment by a professional director. This work suggests a pathway to the use of Machine Learning tools in the industry and could be viewed as an innovative method for automating performance assessment with real-world applications that extend across the film-making process.

关键词： Accuracy Entertainment industry Machine learning Benchmark testing Activity recognition Ubiquitous computing Libraries

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Hazem Qattouz Sattam Almatarneh Khalid Mansour Raed Alazaidah Olla Bulkrock Computer Information Science Dept Higher Colleges of Technology Abu Dhabi UAE Software Engineering Dept Princess Sumaya University for Technology Amman Jordan Data Science & Artificial Intelligence Dept Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Faculty of Computer Studies Arab-Open University Bahrain Bahrain

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment.

关键词： Support vector machines Plant diseases Pathogens Machine learning algorithms Pesticides Soil Cameras Real-time systems Mobile applications Convolutional neural networks

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Transformative Applications and Key Challenges of Generative AI

Transformative Applications and Key Challenges of Generative...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI.

关键词： Power engineering Accuracy Generative AI Education Chatbots Market research Problem-solving Reliability

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sakthitharan Subramanian Gandrakoti Lokeshwari S. Jagadeesan S. Leela V. G Pratheep Murugesan Venkatasudhahar Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology School of Computing Chennai Tamil Nadu India Department of Cybersecurity Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Nandha Engineering college(Autonomous) Erode Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechatronics Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques.

关键词： Deep learning Uncertainty Smart cities Sensor fusion Inference algorithms Bayes methods Internet of Things Reliability Kalman filters Streams

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Model-driven Multi-objective Optimization Approach to 6G Network Planning 15

Model-driven Multi-objective Optimization Approach to 6G Net...

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15th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2021

作者： Petrovic, Nenad Al-Azzoni, Issam Blank, Julian University of Nis Faculty of Electronic Engineering Aleksandra Medvedeva 14 Nis18000 Serbia Al Ain University Department of Software Engineering and Computer Science Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East Lansing United States

ISBN: (纸本)9781665444422

Ultra high-speed and reliable next-generation 6G mobile networks are recognized as key enablers for many innovative scenarios in smart cities - from vehicular use cases and surveillance to healthcare. However, deployment of such network requires tremendous amount of time and involves various costs. For that reason, optimal network planning is of utmost importance for development of 6G mobile networks in smart cities. In this paper, we explore the potential of multi-objective linear optimization in synergy with model-driven approach in order to achieve efficient network planning in smart cities. As outcome, a solution relying on pymoo is proposed and compared to previous works relying only on single objective implemented in AMPL. According to the achieved results, this approach speeds up the execution, while giving more flexibility when it comes to cost/performance trade-offs. © 2021 IEEE.

关键词： Multiobjective optimization

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The future of E-commerce in the publishing industry 2

The future of E-commerce in the publishing industry

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Yusoff, M. Hafiz Pathmanathan, P. Ravindran Latiff, Nurul Adilah Abdul Jusoh, Julaily Aida Abu-Ulbeh, Waheeb El-Ebiary, Yousef A. Baker Al Moaiad, Yazeed International Institute of Applied Science Swiss School of Management Netherlands Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia UMT Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia

ISBN: (纸本)9781665432221

In comparison to conventional book-printed content, electronic "publishing"has allowed users to import files to either a personal computer or an e-book reader to read text in a digital format. Reduced printing costs and reduced time to market by streamlined drafting, proofing, and printing methods are the benefits of the publishing industry over the conventional printed book (p-book). It prevents binding, distribution and shipping. The intent of this paper is to explore the strategic use of e-commerce in the transformation of the publishing industry. The first part of the paper discusses the strategic use of e-commerce/Web-Based Business in the transition from a single distribution channel mass producer of printed books to a multiple distribution channel that will include e-books and print on demand technology in the publishing industry. Emphasis is put on management techniques in the global marketplace for the use of emerging information technologies. The second section of this paper addresses the transfer in competitive influence between the different levels of the supply chain of the publishing industry, including the latest trends in electronic publishing from the stakeholders' point of view. Expected to serve as the base for development within the digitized print industry. As e-publishing becomes popular and moves towards maturity, to allow a "mainstream"status, common standards must emerge and combine. © 2021 IEEE.

关键词： Electronic publishing

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Smarter Movement Control Application 5

Smarter Movement Control Application

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Hafeez, Yasir Ali, Syed Saad Azhar Moiana, Isaias Johane Shaheen, Ateeq-Ur-Rehman Adil, Syed Hasan Amin, Hafeez Ullah University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia Universiti Teknologi Petronas Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia University of Sargodha College of Engineering and Technology Department of Electrical Engineering Punjab Sargodha40100 Pakistan Iqra University Software Engineering Department Karachi Pakistan

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

The coronavirus disease 2019 (Covid-19) has devastated the entire globe in a short period of time and was declared a global pandemic by the World Health Organization (WHO) on March 11th, 2020. It rapidly increased in fatality rate and has become an international public health crisis, culminating in social and economic calamity. However, mobile applications are being introduced globally to minimize the coronavirus's continuous spread by tracing people's circulation or mobility using digital software and smartphones. There is no digitally equipped tool that monitors the movement of the people, particularly in the public places. This work on Smarter Movement Control Application (SMCApp) aims to develop methods that will assist Malaysian people to move around with the aid of mobile tracing application. Therefore, the SMCApp was designed and developed as a mobile application software which stands to improve compliance with the mandated SOP measures across the country, as well as to provide digital support to those who wish to travel to various parts of the country. As a result, it is concluded that the usage of Smarter Movement Control Application (SMCApp) is hoped to bring about safe and effective movement of people throughout these two regions, as it will constantly alert individuals of any suspicious close-contact or the state of the location. Once the application is in effect, with at least 90% of users installing the App on their smartphones, it is projected to increase tranquility and elevate compliance with SOP measures. © 2022 IEEE.

关键词： COVID-19

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Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review

arXiv

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arXiv 2024年

作者： Hassani, Hossein Razavi-Far, Roozbeh Saif, Mehrdad Lin, Liang Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick FrederictonNBE3B 5A3 Canada Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China

Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decision environment to improve its behavior through the reward it receives from the environment. This learning paradigm is, however, well-known for being time-consuming due to the necessity of collecting a large amount of data, making RL suffer from sample inefficiency and difficult generalization. Furthermore, the construction of an explicit reward function that accounts for the trade-off between multiple desiderata of a decision problem is often a laborious task. These challenges have been recently addressed utilizing transfer and inverse reinforcement learning (T-IRL). In this regard, this paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through T-IRL. Following a brief introduction to RL, the fundamental T-IRL methods are presented and the most recent advancements in each research field have been extensively reviewed. Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies for the efficient transfer of knowledge from source domains to the target domain under the transfer learning scheme. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years. © 2024, CC BY.

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