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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Anand, Mrinal Deepak, Gerard Santhanavijayan, A. R.V College of Engineering Bangalore India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819946334

In the current era, recipe recommendation is of extreme significance as there is no expert intelligence system specifically for recommendation. In the era of Web 3.0, semantically driven frameworks are mostly absent. As a result, semantically infused recipe recommendation model is highly important. This study proposes a semantically infused intelligent approach to recommend recipes using Ontology Focused Machine Intelligence to classify the documents. The semantic similarity was computed by hybridizing Normalized Compression Distance (NCD) and KL divergence under a cultural algorithm. The model uses both Recurrent Neural Network (RNN) and XGBoost at different locations to sort the data and compute semantic similarity. The model yielded an FDR of 0.04, and an accuracy of 97.09%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ontology

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Quantum Neural Network for Time Series Forecasting: Harnessing Quantum Computing's Potential in Predictive Modeling 2

Quantum Neural Network for Time Series Forecasting: Harnessi...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Balakrishnan, D. Mariappan, Umasree Raghavendra, Pagadala Geetha Manikanta Reddy, Pallela Karthikeya Dinesh, Rayavarapu Lakshmi Narasimha Jabiulla, Shaik Bugganapalli Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu 626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626005 India

ISBN: (纸本)9798350308846

Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications. © 2023 IEEE.

关键词： Machine learning Neural network Prediction model Predictive features Quantum computing Time series

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Boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Yuvalatha, S. Nithyapriya, S. Kuzhali, R. Tamizh Savitha, S. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science C K college of Engineering and Technology Chennai India Department of Computer Science nad Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798350305708

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease. © 2023 IEEE.

关键词： Adaboosting Boruta Algorithm Cardio vascular Hyper parameter

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An Efficient Approach for Obstacle Avoidance and Navigation in Robots

An Efficient Approach for Obstacle Avoidance and Navigation ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Phani Shanmukh, M. Natarajan, B. Kannan, C. Tamilselvi, M. Vigneshwaran, T. Husain, S. Syed Chennai India Vit University School of Computer Science and Engineering Chennai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Roever Engineering Department of Computer Science and Engineering Perambalur India School of Engineering and Technology Department of Cse Bangalore India K Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamilnadu Tiruchirapalli India

ISBN: (纸本)9798350315455

Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focusing on Bayesian, static, dynamic policy, Deep Q-Learning (DQN) and extended dynamic policy algorithms. In the context of robot obstacle avoidance, these algorithms enable an agent to interact with its physical environment, learns effective operating strategies, and optimize actions to maximize a reward signal. The environment typically consists of a physical space that the robot must navigate without encountering obstacles. The reward signal serves as an objective measure of the robot's performance towards accomplishing specific goals, such as reaching designated positions or completing tasks. Furthermore, successful obstacle avoidance strategies acquired in simulation environments can be seamlessly transferred to real-world scenarios. The promising results achieved thus far indicate the potential of reinforcement learning as a powerful tool for enhancing robot obstacle avoidance. This research concludes with insights into the future prospects of reward learning, high-lighting its ongoing importance in the development of intelligent robotics systems. The proposed algorithm DQN outperforms well among all the other algorithms with an accuracy of 81%, Through this research, we aim to provide valuable insights and directions for further advancements in the field of robot obstacle avoidance using reinforcement learning techniques. © 2023 IEEE.

关键词： Bayesian algorithms DQN algorithm dynamic policy extended dynamic policy physical environment real-world transfer. reinforcement learning reward signal robot obstacle avoidance simulation static algorithms

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An Efficient, Affordable and Reliable SMPS Battery Chargers for Automobile Industry 2

An Efficient, Affordable and Reliable SMPS Battery Chargers ...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Niveditha, J. Venkatesh, T. Reethika, A. Ramya, U. Sreejaa, S. Vinothini, V. Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

As we are utilizing Conventional vehicles, Electric Vehicles require charging the batteries, so the chargers for batteries are becoming more and more crucial within the automobile industry. While the current chargers mostly rely on bulky and inefficient transformer models, this proposed work aims to explore the integration of Switched-Mode Power Supply (SMPS) technology. The method employed in this work is that replaces the charging based on transformers with SMPS technology, with the goal of improving efficiency, reducing size, and reducing heat generation. The result of the project has a successfully developed battery charger that utilizes SMPS technology. It has achieved increased efficiency, reduced size, and lower heat generation in comparison to traditional models. Furthermore, the revised charger exhibits a simpler and more affordable circuit, catering to a wider consumer base. As a result, this product can be used in both workshops and automobile showrooms with more efficiency at an affordable cost. © 2024 IEEE.

关键词： Automotive industry

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A Comprehensive Survey of Recent Transformers in Image,Video and Diffusion Models

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computers, Materials & Continua 2024年第7期80卷 37-60页

作者： Dinh Phu Cuong Le Dong Wang Viet-Tuan Le College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Faculty of Information Technology Yersin University of Da LatDa Lat66100Vietnam Faculty of Information Technology Ho Chi Minh City Open UniversityHo Chi Minh City722000Vietnam

Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention *** study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion *** begin by discussing existing surveys of vision transformers and comparing them to this ***,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,*** the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for *** also provide a comprehensive overview of recent transformer models for video tasks and diffusion *** compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark ***,we explore some future research directions to further improve the field.

关键词： Transformer vision transformer self-attention hierarchical transformer diffusion models

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Optimal Multi-Keyword Ranked Search Matching in Public Cloud with Enhanced Secure Search Scheme Over Encrypted Data 9

Optimal Multi-Keyword Ranked Search Matching in Public Cloud...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Radha, B. Jaya Bharathi, R. Vidhya Muthulakshmi, R. Vidhya, K. Reddy, R. Vijaya Kumar Rondon, Ruchika Sri Krishna Arts And Science College Department of Ict And Cognitive Systems Tamil Nadu Coimbatore641008 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600 123 India Kpr Institute of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Arasur641407 India Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522502 India Graphic Era Deemed To Be University Department of Petroleum Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350365092

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems. © 2024 IEEE.

关键词： Data privacy

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Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

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computers, Materials & Continua 2022年第2期70卷 2113-2130页

作者： Awais Khan Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxila47080Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric *** main challenges in gait recognition are the change in in-person view angle and covariant *** major covariant factors are walking while carrying a bag and walking while wearing a *** learning is a new machine learning technique that is gaining *** techniques for HGR based on deep learning are presented in the *** requirement of an efficient framework is always required for correct and quick gait *** proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this *** proposed framework consists of four primary *** the first step,the database is normalized in a video *** the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s *** that,we trained both modified models using transfer learning and extracted the *** IACO algorithm is used to improve the extracted *** is used to select the best features,which are then passed to the Cubic SVM for final *** cubic SVM employs a multiclass *** experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.

关键词： Gait recognition deep learning transfer learning features optimization classification

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Boosted Spider Wasp Optimizer for High-dimensional Feature Selection

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Journal of Bionic engineering 2024年第5期21卷 2424-2459页

作者： Elfadil A.Mohamed Malik Sh.Braik Mohammed Azmi Al-Betar Mohammed A.Awadallah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman University346AjmanUnited Arab Emirates Department of Computer Science Al-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Technology Al-Balqa Applied UniversityIrbid21510Jordan Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman University346AjmanUnited Arab Emirates

With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult *** is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between *** of the Feature Selection(FS)approaches now in use for these problems perform sig-nificantly less well when faced with such intricate situations involving high-dimensional search *** is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of *** paper presents a new binary Boosted version of the Spider Wasp Optimizer(BSWO)called Binary Boosted SWO(BBSWO),which combines a number of successful and promising strategies,in order to deal with *** shortcomings of the original BSWO,including early convergence,settling into local optimums,limited exploration and exploitation,and lack of population diversity,were addressed by the proposal of this new variant of *** concept of chaos optimization is introduced in BSWO,where initialization is consistently produced by utilizing the properties of sine chaos mapping.A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and *** exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search ***,quantum-based optimization was added to enhance the diversity of the search agents in *** proposed BBSWO not only offers the most suitable subset of features located,but it also lessens the data's redundancy *** was evaluated using the k-Nearest Neighbor(k-NN)classifier on 23 HFS problems from the biomedical domain taken from the UCI *** results were compared with those of traditional BSWO and other well-known meta-heuristics-based *** findings indicate that,in comparison to other competing techn

关键词： High-dimensional features SWO algorithm Feature selection Optimization Machine learning

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Design and Analysis of Deep Learning based Water Potability Prediction

Design and Analysis of Deep Learning based Water Potability ...

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

作者： Arumugam, M. Vijayakumar, P. Kannan, A.C. Velmurugan, J. Arun, G. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - Pg Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi638057 India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300857

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples. © 2023 IEEE.

关键词： Potable water

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