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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Mahanta, Ajoy Bin Nur, Md. Oshama Islam, Rakibul Azad, Saiful Wadith, Shadman Mahmud, Mufti Kaiser, M. Shamim Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Nottingham Trent University Department of Computer Science NottinghamNG118NS United Kingdom Nottingham Trent University Computing and Informatics Research Centre NottinghamNG118NS United Kingdom Nottingham Trent University Medical Technologies Innovation Facility NottinghamNG118NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9798350394290

Metaheuristic algorithms play a pivotal role in solving complex optimization problems. This study addresses a common challenge faced by such algorithms, i.e., getting trapped in local optima. This problem hampers the efficacy of metaheuristic algorithms in achieving globally optimal solutions. This limitation is also observed in Hybrid Henry Gas Solubility Optimization Algorithm (HHGSO)-a new metaheuristic based algorithm-which employs a cluster-based approach for population management. While this approach fosters exploration by diversifying the search process, it lacks adaptiveness in cluster assignments and the potentiality of hybridization with other algorithms. These shortcomings hinder the algorithm's ability to effectively escape local optima and harness the strengths of diverse optimization techniques. To mitigate these challenges, this study proposed an Enhanced HHGSO (EHHGSO) algorithm, an advanced hybridization of HHGSO. Here, a novel backtracking based technique is incorporated that can detect the trapping into local optima and can escape from it. Empirical findings substantiate the efficacy of EHHGSO in comparison to its ancestor, HHGSO for various benchmark functions. © 2023 IEEE.

关键词： Solubility

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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Networking, Electrical engineering, computer Science, and technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition

Patch-NetVLAD+: Learned patch descriptor and weighted matchi...

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2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2022

作者： Cai, Yingfeng Zhao, Junqiao Cui, Jiafeng Zhang, Fenglin Feng, Tiantian Ye, Chen Tongji University School of Electronics and Information Engineering Department of Computer Science and Technology Shanghai201804 China Tongji University Institute of Intelligent Vehicle Shanghai201804 China Tongji University School of Surveying and Geo-Informatics Shanghai200092 China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific region (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding the LSRs that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 9.3% performance improvement than existing patch-based methods. © 2022 IEEE.

关键词： Location awareness Visualization Aggregates Semantics Intelligent systems IEEE Regions

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Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization

arXiv

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arXiv 2024年

作者： Luo, Wenjian Xu, Peilan Yang, Shengxiang Shi, Yuhui School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Informatics The De Montfort University LeicesterLE1 9BH United Kingdom Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, MPMOPs remain understudied in comparison to conventional multiobjective optimization. The competition aims to address this gap by encouraging researchers to explore tailored modeling approaches. The test suite comprises two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the first part are evaluated using Multiparty Inverted Generational Distance (MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV) metrics. The average algorithm ranking across all problems serves as a performance benchmark. © 2024, CC BY-NC-SA.

关键词： Multiobjective optimization

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Telecom Churn Analysis using Machine Learning in Smart Cities

Telecom Churn Analysis using Machine Learning in Smart Citie...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ashish Sharma Prafullit Shukla Mahendra Kumar Gourisaria Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura UP India School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

With the increase in the Telecom industry, service providers are more attentive toward the action of becoming larger or more extensive to the subscriber base. For surviving in telecom companies, the continued possession of holding customers must be a big challenge. According to consideration in the telecom environment, the market price of obtaining the new purchaser is more than holding the existing purchaser. Through collecting knowledge from the telecom industry to analyze the association of the customer whether will leave or not the company. Such types of the Decision tree and Logistic regression model have been compared on the 3334 instances of the dataset. The classification model derived from logistic regression has an accuracy of 80% and the decision tree classifier with an accuracy of 97%.

关键词： Industries Training Support vector machines Analytical models Smart cities Databases Companies

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm

Psychological Stress Classification Using Extreme Gradient B...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Maria Mahardini Sakanti Viacheslav Siniaev Aurelia Amaris Win-Jet Luo Suhartono C. Bambang Dwi Kuncoro Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taichung Taiwan Institute of Applied Computer Science ITMO University St. Petersburg Russian Federation Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taichung Taiwan Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.

关键词： Accuracy Microcontrollers Psychology Human factors Predictive models Boosting Prediction algorithms Real-time systems Sensors Wearable devices

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Unsupervised Fact-Checking via Recursively Verifying Presuppositions

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2189-2199页

作者： Xiucheng Lyu Runcong Zhao Jiazheng Li Bin Liang Min Yang Lin Gui Ruifeng Xu School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China Department of Informatics King’s College London London U.K. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong SAR China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China

Presuppositions are implicit assumptions that interlocutors take for granted. Verifying presuppositions is crucial for reasoning-based fact-checking that aims to reason about True or False, but existing pipelines have overlooked it. We first raise this critical issue with the community and propose a system that addresses the generation, verification, and reasoning of presuppositions through a recursive question-answering process. Importantly, our system automatically determines which knowledge entails which presuppositions, making it an unsupervised system for evidence retrieval, fact-checking, and justification production. We conduct a comprehensive experiment on the FEVER, Vitamin C, and FEVEROUS-S datasets. Our system outperforms competitive baselines, including LLM, and achieves state-of-the-art unsupervised performance. We highlight this critical issue to encourage further research on reasoning-based fact-checking.

关键词： Cognition Training Speech processing Pipelines Electronic mail Production Semantics Tree traversal Security Reliability

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Brain Tumor Detection System Using Improved Convolutional Neural Network 6

Brain Tumor Detection System Using Improved Convolutional Ne...

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6th International Conference on Image Information Processing, ICIIP 2021

作者： Kumar, Raj Singh, Anupam Datta, Goutam Kumar, Ashwani Garg, Harshit KIET Group of Institutions Delhi-NCR Department of Computer Science Ghaziabad India UPES Informatics SoCS Dehradun India Sreyas Institute of Engineering and Technology Hyderabad India ABES IT Department of Computer Science Ghaziabad India

ISBN: (纸本)9781665433617

Brain tumors are taken into consideration to be an extreme form of ailment with inside the medical field. Brain Tumors are a purpose for the peculiar and out of control division and growth of cells with inside the brain region itself. If this out-of-control increase will become greater than 60% then the affected person is not able to recover. Human inspection is the usual approach for detecting any contamination in MR brain images. This technique is impractical for a big quantity of data. Therefore, computerized tumor detection strategies are advanced as they might keep radiologists time. The step for tumor detection begins off evolved with the acquisition of an MRI test photo of the tumor. MRI images have grey and white matter and the vicinity affected by the tumor is of excessive intensity. The proposed work is split into four parts as preprocessing, feature extraction, augmentation after which classification has finished the usage of a machine learning algorithm. © 2021 IEEE.

关键词： Magnetic resonance imaging

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach

AI Based Prediction for Heart Disease: A Comparative Analysi...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Jay Raval Jai Prakash Verma Sardar N. M. Islam Rachna Jain Narina Thakur Computer Science Engineering Department Institute of Technology Nirma University Ahmedabad India Applied Informatics Research Victoria University Melbourne Australia Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm.

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Blockchain-Enhanced Energy-Efficient Architectures for Sustainable Internet of Things Ecosystems 1st

Blockchain-Enhanced Energy-Efficient Architectures for Susta...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Goyal, S.B. Rajawat, Anand Singh Verma, Chaman Illés, Zoltán Singh, Jaiteg Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Sciences and Engineering Sandeep University Nashik India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

Novel technique are required to ensure the long-term viability and maximize the efficiency of energy consumption in the constantly changing Internet of Things (IoT) landscape. In order to accomplish these objectives, this study examines the potential use of blockchain technology into IoT architectural concepts. Our approach utilizes blockchain technology to effectively manage and enhance energy consumption in IoT devices and networks. This is achieved by capitalizing on the decentralized, secure, and transparent characteristics of blockchain. Our objective is to do this by developing a decentralized ledger system that documents all energy transactions and interactions inside an Internet of Things (IoT) environment. This strategy utilizes blockchain technology to provide accountability and real-time monitoring of energy consumption, hence facilitating improved energy allocation and consumption. We delve into the concept and execution of smart contracts that ensure transactions and automate energy-conservation procedures, thereby diminishing the necessity for centralized authority and alleviating energy wastage. In addition, we examine the possibility of blockchain technology to enable direct energy transfer between Internet of Things (IoT) devices. This feature facilitates the development of a self-sustaining Internet of Things ecosystem by enabling devices with surplus energy to vend it to those requiring it. We also tackle the difficulties associated with the integration of blockchain with IoT, such as the energy consumption resulting from blockchain activities and the ability to effectively handle large volumes of data. Furthermore, we offer resolutions to these problems. The proposed framework improves the energy efficiency of IoT ecosystems and promotes their sustainability by advocating for the use of renewable energy sources and decreasing carbon footprints. Through the utilization of simulations and real-world illustrations, we showcase the efficacy of our bl

关键词： Clean energy

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