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IEEE International Conference on Robotics and Automation (ICRA)

作者： Deniz Kerimoglu Daniel Soto Malone Lincoln Hemsley Joseph Brunner Sehoon Ha Tingnan Zhang Daniel I. Goldman School of Physics Georgia Institute of Technology Atlanta USA Department of Applied Physics and Engineering Morehouse College Atlanta USA School of Interactive Computing Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Future planetary exploration missions will require reaching challenging regions such as craters and steep slopes. Such regions are ubiquitous and present science-rich targets potentially containing information regarding the planet’s internal structure. Steep slopes consisting of low-cohesion regolith are prone to flow downward under small disturbances, making it challenging for autonomous rovers to traverse. Moreover, the navigation trajectories of rovers are heavily limited by the terrain topology and future systems will need to maneuver on flowable surfaces without getting trapped, allowing them to further expand their reach and increase mission *** this work, we used a robophysical rover model and performed maneuvering experiments on a steep granular slope of poppy seeds to explore the rover’s turning capabilities. The rover is capable of lifting, sweeping, and spinning its wheels, allowing it to execute leg-like gait patterns. The high-dimensional actuation capabilities of the rover facilitate effective manipulation of the underlying granular surface. We used Bayesian Optimization (BO) to gain insight into successful turning gaits in high dimensional search space and found strategies such as differential wheel spinning and pivoting around a single sweeping wheel. We then used these insights to further fine-tune the turning gait, enabling the rover to turn nearly 90 degrees at just above 4 seconds with minimal downhill slip. Combining gait optimization and human-tuning approaches, we found that fast turning is empowered by creating anisotropic torques with the sweeping wheel.

关键词： Systematics Navigation Wheels Turning Bayes methods Trajectory Topology

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Comparison of various Supervised Machine Learning Algorithms in Sentiment Analysis of Tweets on Black Fungus 12

Comparison of various Supervised Machine Learning Algorithms...

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12th International Conference on computing Communication and Networking Technologies, ICCCNT 2021

作者： Preethi, S. Ashvika Saroja, G. Department of Computing Coimbatore Institute of Technology Coimbatore India Department of Applied Mathematics and Computational Sciences P S G College of Technology Coimbatore India

ISBN: (纸本)9781728185958

People tend to share thoughts in their point of views about a specific or a random event that is happening all over the world in the form of texts, posts, images, videos and audios through social media. This in turn reflects the overall emotion or sentiment of the society regarding a particular social event. In this work, the tweets of various users across the world in a certain period regarding the spread of mucormycosis disease which spreads over in the areas of India recently is collected and the text or the tweets were processed using several natural language processing tools. The linear support vector machine model is built in order to classify the tweets of the users. © 2021 IEEE.

关键词： Sentiment analysis

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An Improved Approach for Semantic Segmentation of Fundus Lesions using R2U-Net 46

An Improved Approach for Semantic Segmentation of Fundus Les...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Pereira, Alejandro Santos, Carlos Aguiar, Marilton Welfer, Daniel Dias, Marcelo De Menezes, Rafaela Ferreira, Paulo Roberto Rossi, Fábio D'Ornellas, Marcos Haygert, Carlos Jesus Kazienko, Juliano Comim, Fabio Hoppe, Aurélio Weber, Juliano Tavares, Leandro Guths, Leonardo Guerra, Rodrigo Federal University of Pelotas - UFPel Postgraduate Program in Computing Pelotas Brazil Science and Technology Farroupilha - IFFar Federal Institute of Education Alegrete Brazil Federal University of Santa Maria - Ufsm Department of Applied Computing Santa Maria Brazil Federal University of Santa Maria - Ufsm Department of Clinical Medicine Santa Maria Brazil Federal University of Santa Maria - Ufsm Industrial Technical College Santa Maria Brazil Federal University of Minas Gerais - Ufmg Department of Clinical Medicine Belo Horizonte Brazil Regional University of Blumenau - Furb Department of Information Systems and Computing Blumenau Brazil Science and Technology Farroupilha - IFFar Federal Institute of Education Santo Ângelo Brazil Federal University of Rio Grande do Sul - Ufrgs Postgraduate Program in Computing Porto Alegre Brazil Federal University of Rio Grande - Furg Center of Computational Sciences Rio Grande Brazil

ISBN: (纸本)9798350371499

Diabetic Retinopathy (DR) is a microvascular complication related to diabetes that affects approximately 33% of individuals with this condition and, if not detected and treated early, can lead to irreversible vision loss. Fundus lesions such as Hard and Soft Exudates, Hemorrhages, and Microaneurysms typically identify DR. The development of computational methods to segment these lesions plays a fundamental role in the early diagnosis of the disease. This paper proposes a new approach that uses an R2U-Net combined with data augmentation techniques for segmenting fundus lesions. We trained, adjusted, and evaluated the proposed work in the DDR dataset, achieving an accuracy of 99.87% and a mean Intersection over Union (mIoU) equal 59.69%. Furthermore, we assessed it in the IDRiD dataset, achieving an mIoU of 49.92%. The results obtained in the experiments highlight the potential contribution of the model in the lesion annotations for creating new DR datasets, which is essential given the scarcity of annotations in publicly available datasets. © 2024 IEEE.

关键词： Semantic Segmentation

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Artificial Intelligence based Healthcare Knowledge Structure for Automatic Medical Report Generation 7

Artificial Intelligence based Healthcare Knowledge Structure...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Kumar, Pramod Alghayadh, Faisal Yousef Tamta, Vinay Prasad Anand, Monika Soni, Mukesh Chaudhary, Jitendra Kumar Ganga Institute of Technology and Management Bhadana- Bahadurgarh Road Kablana Haryana Jhajjar India AlMaarefa University Computer Science and Information Systems Department College of Applied Sciences Riyadh Saudi Arabia Department of Information Technology Uttarakhand Srinagar India Chandigarh University Department Cse Punjab Mohali India Lovely Professional University Division of Research and Development Punjab Phagwara India Graphic Era Hill University School of Computing Bhimtal Campus Uttarakhand India

ISBN: (纸本)9798350350067

Automatic medical report production is a key text summarization application. Traditional text summarizing systems cannot completely interpret and use medical texts' complicated vocabulary due to the disparities between medical consultation data and general domain data. The crucial knowledge in medical consultations has not been successfully harnessed. Traditional text summary algorithms cannot automatically identify, and filter key information based on medical reports' structure and generate structured text. An AI-based knowledge-assisted structured medical report creation solution addresses these difficulties. This method uses entity-guided previous domain knowledge and structure-guided task decoupling to fully utilize medical consultation data and medical report structure. The suggested strategy improves ROUGE scores by 2% to 3% compared to similar methods, resulting in more accurate medical reports, according to IMCS21 dataset experiments. © 2024 IEEE.

关键词： Electronic health record

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Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

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Intelligent Automation & Soft computing 2023年第1期35卷 367-380页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Abdul Rahaman Wahab Sait Hazim Saleh AlRawashdeh Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing University Name:Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia Department of Cyber Security Onaizah Private CollegesUnaizah51911Kingdom of Saudi Arabia

Wireless sensor network(WSN)plays a vital part in real time tracking and data collection *** incorporates a set of numerous sensor nodes(SNs)commonly utilized to observe the target *** SNs operate using an inbuilt battery and it is not easier to replace or charge ***,proper utilization of available energy in the SNs is essential to prolong the lifetime of the *** this study,an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection(TFL-BOARS)has been developed for clustered *** TFL-BOARS technique intends to optimally select the cluster heads(CHs)and routes in the clustered ***,the TFL-BOARS technique incorporates Type-II Fuzzy Logic(T2FL)technique with distinct input parameters namely residual energy(RE),link quality(LKQ),trust level(TRL),inter-cluster distance(ICD)and node degree(NDE)to select CHs and construct ***,the butterfly optimization algorithm based route selection(BOARS)technique is derived to select optimal set of routes in the *** addition,the BOARS technique has computed afitness function using three parameters such as communication cost,distance and *** order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN,a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

关键词： Type II-fuzzy logic uncertainty WSN energy lifetime route selection

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Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

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Journal of Mobile Multimedia 2022年第2期18卷 325-348页

作者： Vinoth Kumar, V. Karthick Raghunath, K.M. Rajesh, N. Venkatesan, Muthukumaran Joseph, Rose Bindu Thillaiarasu, N. School of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India University of Technology and Applied Science Shinas Oman Department of mathematics School of Applied Sciences REVA University Karnataka Bengaluru India Department of Mathematics Christ Academy Institute for Advanced Studies Bangalore India School of Computing and Information Technology REVA University Bengaluru India

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model. © 2021 River Publishers

关键词： Accuracy Blast Bronzing Convolution neuro-fuzzy Crown rot Disease classification Head blight Paddy

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Powerpoint Slide Presentation Control Based on Hand Gesture 3rd

Powerpoint Slide Presentation Control Based on Hand Gesture

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Kumar, Ankit Singh, Kamred Udham Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Uttarakhand Dehradun248007 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819773558

HCI, which stands for "human–computer interaction," has been increasingly concerned with natural interaction methods in recent years. Various ways we interact with computers have benefited from the development of real-time hand gesture recognition programs. The detection of hand motions calls for the use of a camera. The primary form of participation is the employment of a web camera as a virtual human–computer interaction device. In this body of work, we look into how vision-based HCI methods of the present day make use of hand gestures. In the event that consumers are unable to utilise any input device or touch it, this project becomes incredibly useful. Gesture recognition makes it possible to complete an activity without physically accessing the usual input devices (mouse, keyboard, etc.). The user may doodle with his index finger and use his index and middle fingers together to control the pointer's movement on the screen. The user may erase their artwork using the tips of their index, middle, and ring fingers. Using their little finger, the user may go on to the next file, and using their thumb finger and pointing to the left, they can return to the previous file. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gesture recognition

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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Computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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Artificial Intelligence in Financial Derivatives: A Comparative Analysis of PSO and ACO for American-Style Put Options

Artificial Intelligence in Financial Derivatives: A Comparat...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Afroza Akter Salma Parvin A B M Shahadat Hossain MD Morshedul Islam Department of Mathematics Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Mathematical & Computing Sciences Concordia University of Edmonton Edmonton AB Canada

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Artificial Intelligence (AI) enhances option pricing accuracy and risk management, enabling precise valuation of complex derivatives across various market scenarios. This paper compares Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in valuing American-style put options. We apply these metaheuristic algorithms to tackle the complexity of option pricing due to the early exercise feature, evaluating their performance across various boundary functions. We assess each method's accuracy and computational efficiency through extensive simulations. These simulations are benchmarked against findings from existing literature. Python's Computer Algebra System (CAS) is used for the simulations. The findings highlight AI's potential to enhance option valuation and contribute valuable insights for practitioners in finance.

关键词： Accuracy Computational modeling Metaheuristics Finance Pricing Numerical models Risk management Artificial intelligence Particle swarm optimization Cost accounting

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