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Robust Domain Generalization for Multi-modal Object Recognition

Robust Domain Generalization for Multi-modal Object Recognit...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Yuxin Qiao Keqin Li Junhong Lin Rong Wei Chufeng Jiang Yang Luo Haoyu Yang Department of Computer Information Technology Northern Arizona University Flagstaff AZ USA Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Cambridge MA USA Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin Austin TX USA Department of Computer Science University of Southern California Los Angeles CA USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual *** address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion.

关键词： Visualization Automation Instruments Transfer learning Natural languages Training data Learning (artificial intelligence) Benchmark testing Robustness Object recognition

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A Survey on the Landscape of Machine Learning Solutions for Detecting Phishing Attacks

A Survey on the Landscape of Machine Learning Solutions for ...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Shijon Das Mohamed I. Ibrahem Mostafa M. Fouda Department of Computer Science Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Despite the advancements in cybersecurity, phishing attacks continue to pose significant threats to organizations and individuals worldwide. With the advent of time, phishing techniques have become more robust and thus demand more enhanced solutions than the existing ones. This survey paper explores how machine learning (ML) techniques are applied for detecting phishing attacks, offering a comprehensive overview of the challenges faced and the advancements made in the field. The paper discusses the fundamentals of phishing attacks and how ML can be applied to detect those attacks. The paper also discusses the applications of deep learning models and anomaly detection techniques which offer precise results for detecting modern phishing strategies. Finally, this survey addresses the challenges faced by current detection systems and suggests potential areas for future research. The goal of this survey is to provide a comprehensive overview that not only discusses the existing machine learning techniques but at the same time inspires innovation in this domain.

关键词： Surveys Deep learning Technological innovation Accuracy Phishing Organizations Blockchains Internet of Things Anomaly detection

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Accuracy Enhancing Time Limited Model Reduction Framework: Computational Efficiency in Power Systems

Accuracy Enhancing Time Limited Model Reduction Framework: C...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Muhammad Latif Hira Ambreen Muhammad Imran Samra Zafar Sajid Bashir Department of Electrical Engineering Military College of Signals (MCS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electrical Engineering Sir Syed Center for Advanced Studies in Engineering Institute of Technology (SS-CASE-IT) Islamabad Pakistan Henan University School of Computer and Information Engineering Kaifeng China College of Electrical & Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

The analytical and modelling precision required to build modern wind turbines makes them stable, controlled, and observable. This article presents a state-of-the-art method for lowering the model order of a wind turbine driven by a double-fed induction generator, where the rotational speed varies over time. The suggested approach uses state-space representations to set up a comprehensive and systematic framework for analysing wind turbine performance and checking for compliance with grid standards. A time-constrained Gramians-based novel balanced realization approach is employed to reduce the dimensionality of the large-scale wind turbine model. Stability and reduced approximation error are two benefits of the lower-order system. The proposed method also gives researchers an a priori formula for the error-bound, making computations faster and more efficient. Using substantial simulation data, we show that our method improves upon previous methods in optimising models that include wind turbines with changing rotational speeds, taking into account both stability and error computation. To enhance the efficacy and accuracy of their modeling procedures, academics and researchers analyzing wind turbines should incorporate this new methodology.

关键词： Analytical models Accuracy Uncertainty Computational modeling Stability criteria Velocity control Power system stability Approximation error Wind turbines Computational efficiency

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Exploring the Synergy of Artificial and Computational Intelligence for Technological Advancements 1

Exploring the Synergy of Artificial and Computational Intell...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Palarimath, Suresh Maran, Pyingkodi Valsalan, Prajoona Wilfred Blessing, N.R. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331532956

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things. © 2024 IEEE.

关键词： Artificial intelligence computational intelligence Internet of things

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Unraveling Model Inversion Attacks: A Survey of Machine Learning Vulnerabilities

Unraveling Model Inversion Attacks: A Survey of Machine Lear...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Tanzim Mostafa Mohamed I. Ibrahem Mostafa M. Fouda Department of Computer Science Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Model Inversion (MI) attacks are a class of security threats targeting machine learning (ML) systems. These attacks aim to uncover sensitive information that was used to train the ML model, by either reconstructing the original training data or inferring private attributes based on the model’s outputs. This research offers a comprehensive survey of MI attacks with a focus on their operation procedure, real-world implications, and defense strategies against such attacks. Additionally, it examines all MI attack survey papers to date and distinguishes itself through the most extensive and comprehensible categorization of existing MI techniques. Lastly, it highlights the overarching need for establishing strong defensive mechanisms to ensure protection against such advancing threats in cybersecurity.

关键词： Surveys Training data Machine learning Data models Blockchains Internet of Things Protection computer security

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IoT Farm: A Robust Methodology Design to Support Smart Agricultural System Using Internet of Things with Intelligent Sensors Association 7

IoT Farm: A Robust Methodology Design to Support Smart Agric...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Karthiga, R. Brindha Devi, C.L. Janaki, R. Gayathri, C. Samuthira Pandi, S. Shobana, D. R.M.K Engineering College Kavaraipettai Department of IT Tamilnadu Chennai India Department of Computer Science Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science & Humanities Tamilnadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamilnadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350340600

The term 'Smart Farming' has been increasingly popular in recent years due, in large part, to the proliferation of network-based, high-tech farm monitoring cycles. These cycles make extensive use of technologies for communication and information gathering in the form of machinery, equipment, and sensors. The Internet of Things, often known as IoT, is the wave of the future in every sector of the economy, and its impacts will be felt by everyone. It is a collection of different devices that are capable of configuring themselves into a fully operational network. Conventional farming practices are progressively being supplanted by smart farming, which takes use of advancements made possible by the internet of things. This makes conventional farming methods more efficient, which in turn saves farmers money and reduces the amount of food that is lost. This technology helps farmers conserve energy and time by evaluating data in actual time from sensors placed in crops and offering actionable information on how their crops are progressing. The data comes from the sensors themselves, which are planted in the crops. Data from the field is sent to the cloud and evaluated there so that procedures may be automated utilizing Internet of Things devices. The assessment of the farmer's contribution into the process of making choices is demonstrated with information regarding the temperature of the field, the soil moisture level, the humidity level, and the amount of water that is used. In addition, the technical knowledge presented here is valuable to producers at every stage of the production process, including planting, growing, harvesting, packaging and shipping. © 2023 IEEE.

关键词： Internet of things

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Prediction and Electricity Forecasting on the Individual Household Level based on PSO-LSSVM Approach 7

Prediction and Electricity Forecasting on the Individual Hou...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Padmaja, Voleti Tamizhselvi, A. Kathir, I. Mohan, Arvind Pal, Subharun Rastogi, Ravi Geethanjali College of Engineering and Technology Electrical and Electronics Engineering Hyderabad India St. Joseph's College of Engineering Department of Information Technology Chennai India V.S.B. Engineering College Department of Electrical & Electronics Engineering Karur India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir Jagti India NIELIT Electronics Division Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350340600

When smart metering systems are used to encourage energy conservation at the residential level, new research challenges arise in the areas of monitoring usage and providing accurate load forecasts. Intelligent smart meters rely on accurate predictions of future electricity use. Many energy and operational improvements, such as more efficient appliances, renewable energy sources, and advanced metering systems, are included into micro-grids. Here is an alternative approach to estimating annual energy use in single-family homes. Incorporating the effects of residents' daily activities and appliance usages on overall home power consumption improves the accuracy of the forecasting model. Data preparation, feature selection, and model training are the three phases of the suggested approach. Data preprocessing usually entails cleaning the data and preparing the features to be used. When it comes to feature selection, the K-means clustering approach is frequently utilized. After using information gain for feature selection, the models are trained using PSO-LSSVM. The proposed method outperforms both GRU and LSSVM, two well-established competitors. © 2023 IEEE.

关键词： K-means clustering

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A Novel Deep Learning Assisted Lung Nodule Identification Mechanism with Intelligent Image Processing Principles

A Novel Deep Learning Assisted Lung Nodule Identification Me...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Senthil, P. Pandi, V.Samuthira Shobana, D. Jayashankari, J. Priya, J. Lakshmi Kaushik, G. Department of Computer Science and Engineering Cmr College of Engineering & Technology India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350300857

Lung cancer has the greatest fatality rate of all cancers. Due to its efficacy, chest CT has become standard practice for lung cancer screening and diagnosis. Radiologists have a significant challenge when trying to detect nodules in 3D volume imaging due to the tiny size of their shadows. This research study has created a novel computer-Aided Design (CAD) system that can automatically identify tumors in the lungs in CT scans to help with this problem. When it comes to cancers that can be fatal even if treated late, lung cancer is by far the most frequent. At now, CT can be utilized to aid in the early detection of lung cancer. The identification of lung cancer frequently relies on the expertise of medical professionals, who may overlook some patients and so exacerbate the situation. In several fields of medical imaging diagnostics, deep learning has shown to be an effective and widely used technique. This research work proposes an Intelligent Learning based Nodule Detection (ILND), a revolutionary deep learning-based approach to detecting lung cancer. This research study adapts those networks for use in classifying CT images of both malignant and benign lung nodules. The CT imaging dataset was used to assess the performance of the networks. The results of the experiments demonstrate that the suggested Intelligent Learning based Nodule Detection model outperformed the other network models, with an accuracy of 95.67%, a sensitivity of 93.65% and a specificity of 94.82%. © 2023 IEEE.

关键词： computerized tomography

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Performance Analysis Of Insulation Resistance Test And Cable Laying Installation In Library Block

Performance Analysis Of Insulation Resistance Test And Cable...

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Global Power, Energy and Communication Conference (GPECOM)

作者： B. Rubini R. Karthick Kalaiarasi G S. Saranya S. Pradeep Kumar M. Suganiya Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Electrical and Electronics Engineering C.K.College of Engineering and Technology Cuddalore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology & Research (Deemed to be University) Guntur India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

The process of installing electrical cables physically in a specific location inside a library block is widely considered as an electrically equipped place. It involves planning the particular route, securing the cables and ensuring technical support and proper protection. In a library block, the process of installing electrical wires can provide power and electrical connectivity to various devices and systems. An insulation resistance tests were conducted a high voltage flow to the insulation and measures the resistance ratio to the actual current flow after cable installation. Testing of measurements given in table. Algorithms help to identify all kinds of weaknesses and faults in the insulation that could lead to any electrical leakages or short circuits. The insulation resistance test is primarily important to ensure the safety and reliability of electrical systems. It is particularly needed in an environment like a library. Libraries are filled with books made of papers that are easily flammable in nature. So, we should give top priority to electrical safety in libraries. Minimum constrained non-linear minimization solver is used to identify the iteration levels and verified with best function values and first order optimality values. This paper shows performance analysis of Insulation resistance test and Cable laying installation in a Library block.

关键词： Resistance Insulation Current measurement Wires Libraries Performance analysis electrical resistance measurement

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