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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Jai Keerthy Chowlur Revanna Dilli. Mahitha Kalakonda Jyothsna A. Mahendar Manjula B. M Information Systems Engineering and Management Harrisburg University of Science and Technology Harrisburg USA Malla Reddy Engineering College for Women Autonomous Coimbatore India Department of Electronics and Communication Engineering Vaageshwari College of Engineering Karimnagar India Department of Comuter Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In photovoltaic systems, extracting parameters from generated current-voltage data is critical for simulating, controlling, and optimizing their performance. While there are several strategies for accomplishing this task, but they all have limitations. This work provides an improved adaptive differential evolution approach for solar system parameter extraction. A crossover rate sorting mechanism is developed in this system that allocates different crossover rates to individuals based on their fitness, allowing better-performing individuals to have a greater impact on the following generation. A Pelican optimization strategy used to speed up convergence and balance exploration and exploitation. The performance of the proposed method is validated by extracting characteristics from several solar models, including single diode, double diode, and photovoltaic modules. As a result, the proposed method is a viable and efficient option for parameter extraction in photovoltaic models.

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

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Research on the Chain Risk Identification of Large-Scale Skiing Events

Research on the Chain Risk Identification of Large-Scale Ski...

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Chinese Automation Congress (CAC)

作者： Xiaoyu Zhao Xiaobo Sun Shaobo Zhong Shuangfeng Wei School of Geomatics and Urban Spatial Information Beijing University of Civil Engineering and Architecture Beijing China Natural Resources Big Data Center Hebei Geographic Information Group Co. Ltd. Hebei China Institute of Urban Systems Engineering Beijing Academy of Science and Technology Beijing China

Large-scale skiing events are commonly conducted in complex outdoor terrains and weather conditions, where adverse weather conditions can significantly impede the smooth progress of these events. The occurrence of adverse weather conditions increases the risk of domino chains caused by meteorological disasters during large-scale skiing events. To promptly identify the cascading risks caused by adverse weather, this paper proposes a solution to promptly identify cascading risks caused by adverse weather by integrating information related to adverse weather risks of skiing events and constructing a domain knowledge ontology. The paper analyzes and identifies the risk transmission paths of adverse weather, and constructs the risk identification rule base of related event chains to enable global risk chain reasoning driven by spatiotemporal semantics. The proposed approach is demonstrated through the skiing events of the 2022 Beijing Winter Olympics, from which the study correctly identifies the risk events caused by adverse weather encountered in multiple skiing events through reasoning. The results show the accuracy and feasibility of the event chain inference model.

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Boyuan Peng Yiyang Liu Xin Zhu Jie Huang Wenwen Wang Li Fang Qin Zhou Graduate Department of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness.

关键词： Image segmentation Ultrasonic imaging Accuracy Manuals Speckle Semisupervised learning Robustness data models Optimization Medical diagnostic imaging

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Spark based Parallel Frequent Pattern Rules for Social Media data Analytics

Spark based Parallel Frequent Pattern Rules for Social Media...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： Shubhangi Chaturvedi Sri Khetwat Saritha Animesh Chaturvedi Computer Science and Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur (IIITDM Jabalpur) Jabalpur M.P. India Computer Science and Engineering Maulana Azad National Institute of Technology (NIT Bhopal) Bhopal M.P. India Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad (IIIT Dharwad) Dharwad Karnataka India

The number of users on social media are increasing, thus the data produced is also increasing tremendously. Social media data mining and analysis can produce a lot of hidden information, which can be helpful in decision-making. Prediction of the co-occurring words with confidence can provide deep insights of social media. The paper presents an applied process to mine social media dataset to retrieve frequent patterns (or rules) in cost effective time. The retrieved patterns can be useful in making decisions related to social media. The experiment is performed on three social media datasets and various rules are analyzed by varying the values of threshold (minimum support and minimum confidence). Experiments are also performed for both Frequent Pattern (FP) Growth and Parallel FP (PFP) Growth using the same datasets. The parallel computation is achieved with the help of a scalable Apache Spark environment. Execution time for both FP-Growth and PFP-Growth on the same datasets is also described. While performing experiments it is found that FP-Growth of SPMF requires preprocessing to convert item-sets into transactional databases. The pre-processing time is required only once, as a result the time required to generate rules is less. Whereas, the PFP-Growth does not require preprocessing on the dataset to generate rules. This saves time to directly generate the association rules using PFP-Growth.

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English Offensive Text Detection Using CNN Based Bi-GRU Model

English Offensive Text Detection Using CNN Based Bi-GRU Mode...

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information and Communication Technology (ICICT), International Conference on

作者： Tonmoy Roy Md Robiul Islam Asif Ahammad Miazee Anika Antara Al Amin Sunjim Hossain Data Analytics & Information Systems Utah State University Utah United States Computer Science William & Mary Virginia United States Computer Science Maharishi International University Iowa United States Electrical and Electronics Engineering Brac University Dhaka Bangladesh Computer Science Uttara University Dhaka Bangladesh Computer Science Northern University Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models.

关键词： Image resolution Social networking (online) Blogs Text detection information and communication technology Videos

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Exploring Equilibrium Strategies in Network Games with Generative AI

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu The College of Communications Engineering Army Engineering University PLA Nanjing210000 China The School of Computer Science and Engineering NTU Singapore The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 2024, CC BY.

关键词： Game theory

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Lc-Stream: An Elastic Scheduling Strategy with Latency Constrained in Geo-Distributed Stream Computing Environments

SSRN

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SSRN 2023年

作者： Sun, Dawei Wang, Yueru Sui, Jialiang Rong, Jia Gao, Shang Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China Department of Data Science & AI Faculty of IT Monash University VIC3800 Australia School of Information Technology Deakin University Waurn PondsVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

An effective scheduling strategy is critical for achieving better performance in real-time stream processing systems. How to process real-time data stream quickly and efficiently is always a challenging task, especially when clusters are collaborating in a Geo-Distributed computing environment. To address this challenge, we propose an elastic scheduling strategy with latency constrained in Geo-Distributed stream computing environments called Lc-Stream. This paper focuses on the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter-layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo-Distributed computing environment to ensure energy saving under low transmission latency. Our experimental results demonstrate that Lc-Stream reduces the overall latency of typical Geo-Distributed multitasking topology by more than 19%, and increases the throughput by more than 37%. Compared with the existing strategies in Storm, it also helps balance the load between systems and avoid excessive use of computing nodes. © 2023, The Authors. All rights reserved.

关键词： Storms

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN Computer science 2023年第4期4卷 1-10页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Iot Based Smart Assistant Cane For The Visually Impaired

Iot Based Smart Assistant Cane For The Visually Impaired

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： A Harini C Buvana M Harshini S Keerthana D Prabhu S K Umamaheswaran Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu Department of Information Technology Sri Sairam Engineering College Chennai Tamil Nadu Department of Artificial intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai Tamil Nadu Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper presents the design and implementation of a Smart Cane for the visually impaired individuals, incorporating several sensors and features to improve their mobility and safety. The device includes an ultrasonic sensor for obstacle detection, an MPU-6050 device for detecting falls and tilts, a GPS module for navigation, an RTC module for medicine reminders, and a fitness tracking module for monitoring health parameters. Additionally, a Bluetooth headset with a microphone is provided to communicate with the voice assistant in noisy areas. The Smart Cane triggers a buzzer for help and sends a notification to the caretaker in case of a fall or crash. The device is powered by a LIPO battery and is designed to improve the quality of life of visually impaired individuals by providing them with enhanced mobility and safety features.

关键词： Navigation Personal voice assistants Sensor phenomena and characterization Safety Telecommunication computing Noise measurement Intelligent sensors

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