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IEEE International Conference on Networking, Sensing and Control

作者： Junhai Zhou Jianyun Lu Quanwang Wu Junhao Wen College of Computer Science Chongqing University Chongqing China School of Artificial Intelligence and Big Data Chongqing College of Electronic Engineering Chongqing China College of Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665472449

Feature selection is an essential technique which has been widely applied in data mining. Recent research has shown that a good feature subset can be obtained by using evolutionary computing (EC) approaches as a wrapper. However, most feature selection methods based on EC use a fixed-length encoding to represent feature subsets. When this fixed length representation is applied to high-dimensional data, it requires a large amount of memory space as well as a high computational cost. Moreover, this representation is inflexible and may limit the performance of EC because of a too huge search space. In this paper, we propose an Adaptive- Variable-Length Genetic Algorithm (A VLGA), which adopts a variable-length individual encoding and enables individuals with different lengths in a population to evolve in their own search space. An adaptive length changing mechanism is introduced which can extend or shorten an individual to guide it to explore in a better search space. Thus, A VLGA is able to adaptively concentrate on a smaller but more fruitful search space and yield better solutions more quickly. Experimental results on 6 high-dimensional datasets reveal that A VLGA performs significantly better than existing methods.

关键词： Training Sociology Memory management Process control Feature extraction Encoding Sensors

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in artificial intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition artificial intelligence

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An Investigation into the Effectiveness of DNS-Based Authentication for Wireless Networks

An Investigation into the Effectiveness of DNS-Based Authent...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ashu Katyal Rajesh Pandian Ritu Sharma T. Surulivel Rajan Nakul S. Sharma Vaishali Singh Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

the security of Wi-Fi networks is turning into an increasing number, significant with the growth of Wi-Fi technology. To prevent malicious entities from connecting to the network, getting admission to manipulate mechanisms was advanced, which might be based totally on authentication. Conventional authentication mechanisms like passwords are prone to brute-pressure attacks. Numerous options, such as dynamic DNS-primarily based authentication, have been proposed to provide stronger authentication. This research paper investigates the effectiveness of Domain Name System (DNS)-based authentication for wireless networks. The DNS is a critical component of the internet infrastructure and is used to translate domain names into IP addresses. With the rapid growth of wireless networks and the increasing popularity of devices that connect to the internet, securing these networks has become a major concern. Traditional methods of authentication, such as username and password, have proven to be vulnerable to various attacks. This study aims to explore the potential of DNS-based authentication in addressing these security concerns. Through a literature review and analysis of existing authentication protocols, this paper evaluates the strengths and weaknesses of DNS-based authentication. The research also includes a survey of wireless network users to gather their insights and experiences with current authentication methods. The results of this study will provide valuable insights into the effectiveness of DNS-based authentication and its potential as a secure method for wireless network authentication. The effects imply that DNS-primarily based authentication has several wonderful blessings over traditional approaches and may be a powerful mechanism for securing wireless networks.

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FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

arXiv

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arXiv 2023年

作者： Han, Pengchao Shi, Xingyan Huang, Jianwei The School of Information Engineering Guangdong University of Technology Guangzhou510006 China The School of Data Science The Chinese University of Hong Kong Shenzhen China The School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model parameters with others. Each client updates its local model using the average model output/feature of all client models as the target, known as federated KD. However, existing federated KD methods often do not perform well when clients’ local models are trained with heterogeneous local datasets. In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients. First, to alleviate the local model output divergence across clients caused by data heterogeneity, the server acts as a discriminator to guide clients’ local model training to achieve consensus model outputs among clients through a min-max game between clients and the discriminator. Moreover, catastrophic forgetting may happen during the clients’ local training and global knowledge transfer due to clients’ heterogeneous local data. Towards this challenge, we design the less-forgetting regularization for both local training and global knowledge transfer to guarantee clients’ ability to transfer/learn knowledge to/from others. Experimental results show that FedAL and its variants achieve higher accuracy than other federated KD baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation

Secure and Decentralized Collaboration in Oncology: A Blockc...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Ramin Ranjbarzadeh Ayse Keles Martin Crane Shokofeh Anari Malika Bendechache School of Computing Faculty of Engineering and Computing Dublin City University Ireland Department of Artificial Intelligence Engineering Faculty of Engineering and Natural Sciences Ankara Medipol University Ankara Turkiye Department of Accounting Economic and Financial Sciences Islamic Azad University South Tehran Branch Tehran Iran ADAPT Research Centre School of Computer Science University of Galway Ireland

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.

关键词： Image segmentation data privacy Accuracy Machine learning algorithms Annotations Collaboration Medical services

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Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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International Conference on Advances in Information Technology (ICAIT)

作者： M. Anitha V Sheeja Kumari N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and O. Business Systems R.M.D Engineering College Thiruvallur Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting.

关键词： Support vector machines Machine learning algorithms Computational modeling Scalability Software algorithms Big data Prediction algorithms Boosting data models Ensemble learning

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Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model

Predicting Crops Based on Soil and Environmental Characteris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A D. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

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Business Clouds Security: Crafting a Contemporary Adoption Framework

Business Clouds Security: Crafting a Contemporary Adoption F...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Abhishek Bhuva Dipen Bhuva A. Hema D. Anandhasilambarasan G. Gowri Mukesh Soni Department of Computer Science University of Massachusetts Boston Boston Massachusetts USA Department of EECS Cleveland State University Cleveland Ohio USA Department of MCA E.G.S Pillay Engineering College Nagaipattinam Department of Computer science and Engineering Karpagam Academy of higher education Coimbatore Department of Artificial Intelligence and Data science Karpagam Institute of Technology Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cloud computing is becoming more and more popular. Many businesses are making investments in this field, either for internal usage or to offer services to external customers. Ensuring a secure and reliable environmental solution over cloud space is crucial for protecting and safeguarding the transactions conducted by organisations.A cloud computing adoption framework (CCAF) security that works well with business clouds is presented in this *** management, encryption, and firewalling are the three key security approaches that form the foundation of CCAF multilayered security. It is built on improvements in business file sync and share systems. The purpose of the methodology was to illustrate the CCAF multilayered security’ s resilience. In 50 hours of nonstop assault, CCAF multilayered security managed to identify and stop 99.7% of trojans and viruses, and achieve ≥ 80% blocking. The results of the research have been confirmed, and CCAF multilayered security can increase cloud-based big data services’ velocity, amount, and dependability.

关键词： Cloud computing computer viruses Software as a service Robustness Encryption Security Trojan horses

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Streamlining Information Capture and Retrieval with AI-Enabled Systems

Streamlining Information Capture and Retrieval with AI-Enabl...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Gadug Sudhamsu Ankita Agarwal Rekha Dhivrani K.S. Bhanu Jeyalaxmi M Ashwini R. Nawadkar Faculty of Engineering and Technology Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation.

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An Improved Method for Fault Detection Electric Power System Protection using SVMCNN Model

An Improved Method for Fault Detection Electric Power System...

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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： NMG Kumar S.Shanmuga Priya Vineetha Varghese Renuga K Jyoti Prasad Patra Neerav Nishant Dept. of EEE Mohan Babu University Sree Vidyanikethan Engineering College Rangam Pet Tirupathi India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Rathinam Technical campus Coimbatore Tamilnadu India Department of Mathematics St. Joseph's Institute of Technology OMR Chennai Tamilnadu India EE And EEE Krupajal Engineering College-KEC Puri Odisha India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Multiple factors outside our control can contribute to the unanticipated breakdown of electrical power networks. It is of the utmost importance to prevent unforeseen power system issues from affecting other components of the protective system. It is the job of the protective systems to identify and categorize potentially dangerous voltage and/or current lines. To isolate the malfunctioning line, utilize the protection relay to trigger the circuit breaker. An SVM-CNN architecture is presented in this study for the purpose of detecting power outages in transmission lines. When it comes to preprocessing, feature selection, and training models, the correct sequence of activities is crucial. For anomaly-based fault detection algorithms to accurately represent and understand the data, data preprocessing is essential. Feature selection makes the process more feasible by reducing the entire data volume and helping HIF detection home in on the most relevant features. The results were evident with an accuracy improvement reaching 95.70%.

关键词： Training Support vector machines Power transmission lines Fault detection Computational modeling data preprocessing Feature extraction

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