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Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

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IEEE Transactions on Control of Network Systems 2023年第3期10卷 1245-1254页

作者： Bakhshayesh, Babak Ghaffarzadeh Kebriaei, Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran School of Computer Science Tajrish *** Iran

This article presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This article also takes into account the local and the coupling/infrastructure constraints of EVs, along with transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices. © 2014 IEEE.

关键词： Electric power distribution

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Futuristic Diabetes Prediction: Employing Machine Learning for Timely Diagnosis

Futuristic Diabetes Prediction: Employing Machine Learning f...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Kaviyaadharshani, D. Nivedhidha, M. Ramkumar, M.P. Emil Selvan, G.S.R. Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9798350354539

Diabetes is a prevalent circumstance that is also the core cause of numerous serious health issues, such as neurological disorders, heart attack, and stroke. So that it is placing a growing burden in the global healthcare system. The challenge lies in the accuracy of diabetes prediction model. This study centers on constructing an autonomous diabetes prediction system using a private dataset, tackling class imbalance through SMOTE techniques. Leveraging machine learning algorithms the research identifies the most effective prediction method. Feature selection algorithms guide the choice of relevant features, and the system's performance is rigorously evaluated. Remarkably, the developed model achieves impressive metrics with a success rate of 91.41%, showcasing the potential of machine learning in enhancing the prediction accuracy. These findings add vital insights to the profession, emphasizing the need of early intervention in diabetes control. © 2024 IEEE.

关键词： Forecasting

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Classification and Summarization of News Article using Rank SVM and Fuzzy Logic 15

Classification and Summarization of News Article using Rank ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Dhileep, A. Dharshini, T.S. Kumar, S.A. Ashok Kalaivaani, P.C.D. Sathya, S. Kongu Engineering College Computer Science and Engineering Erode India

ISBN: (纸本)9798331300579

Large amounts of unstructured data are currently trending on social media and the Web. Finding pertinent information fast from such a large unstructured corpus is difficult for individuals. Text summarizing is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarizing material by hand requires a lot of time, money, and effort. In the past, researchers have attempted to summarize the text using various machine learning techniques, but their efforts have not yet produced superior summaries. News data is a type of prepared data that can be both structured and unstructured. It contains information such as the author, date, source, location, and headline text. The approach was evaluated on CNN/Daily Mail dataset. These days, we favor the RANK SVM technique, which handles both language and text-related issues, to extract features from the textual data. This paper presents an innovative approach to news text summarization by integrating RANK SVM (Support Vector Machine for Ranking) and fuzzy logic. The system employs machine learning techniques, specifically RANK SVM, to discern and rank relevant sentences based on their importance for summarization. Fuzzy logic is then applied to enhance the processing efficiency by incorporating linguistic and contextual features such as word frequency, sentence length, and the presence of named entities. This synergistic combination aims to capture the nuances of news articles, providing a nuanced representation in the summaries. The results showcase the potential of the combined RANK SVM and fuzzy logic approach to significantly enhance the efficiency and accuracy of news text summarization, addressing the growing need for concise and informative news consumption in today's fast-paced information landscape. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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E-Navigation: The Boon for Visually Impaired

E-Navigation: The Boon for Visually Impaired

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Patil, Leena H. Dahale, Nikhil P. Thakare, Atul L. Fursule, Sumit S. Vaidya, Anurag V. Priyadarshini-College of Engineering Computer Science & Engineering Nagpur India

ISBN: (纸本)9798350348460

Within this paper, we offer a momentous arrangement named "E-navigation: The Boon for Outwardly Disabled"pointed at revolutionizing the lives of people confronting visual disabilities. This groundbreaking framework coordinating ultrasonic sensors, GPS innovation, fire sensors, and area following to offer broad bolster to the outwardly disabled community. By utilizing ultrasonic waves and GPS, this framework empowers impediment location, real-time route, and the sharing of area subtle elements with family individuals. Furthermore, it incorporates fire sensors to upgrade security by alarming clients to potential fire dangers, especially basic for those incapable to outwardly distinguish such perils. Emphasizing reasonableness and effectiveness, this innovation points to enable people with visual impedances over a wide range. With around 39 million individuals all-inclusive hooking with visual inabilities, the potential effect of this route framework is considerable. Its execution guarantees upgraded consolation, expanded speed, and reinforced certainty in route for the outwardly impeded, cultivating more prominent freedom and cooperation in different angles of life. "E-navigation: The Boon for Outwardly Disabled"implies an inventive and promising progression balanced to altogether upgrade the lives of millions confronting challenges due to visual disability. © 2024 IEEE.

关键词： Auditory Feedback GPS Obstacle Detection Sensor Technology Ultrasonic Sensors

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Integrating Pretrained CNN and SVM for Improved Lung Cancer Classification 13

Integrating Pretrained CNN and SVM for Improved Lung Cancer ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Tiwari, Naveen Kumar Bajpai, Abhishek Srivastava, Vivek Yadav, Shashank Tiwari, Neeraj Computer Science and Engineering Rajkiya Engineering College Kannauj India

ISBN: (纸本)9798350305463

Lung Cancer is nowadays becoming a very common disease and a significant reason for death among Indians. Lung cancer often comes in two different forms: small- cell lung cancer and non-small-cell lung cancer. Among all other factors, Smoking cigarettes is one of the major reasons for lung cancer. The body is severely harmed by lung cancer, which is one of the most prevalent and dangerous cancers today. Therefore, the only method of treating lung cancer that is truly successful is early detection and diagnosis. Many lives would be saved if it should be diagnosed at an early stage. Lung Carcinoma, an uncontrolled malignant tumor characterized by uncontrolled cell growth in lung cells, is the alternative name for lung cancer. The paper aims to classify lung cancer in CT scan images. The greatest imaging technique available for medical use, a CT scan can be difficult for physicians to interpret and use to identify cancer. This paper proposes a technique that uses a hybrid method by combining the Support Vector Machine algorithm with CNN (Convolutional neural network) and gives better accuracy. CT scan images of patients are classified as to what type of lung cancer by using this technique. This technique efficiently classifies the cancer into its types. Using a hybrid model (Support Vector Machine algorithm with CNN technique) is an effective way to correctly identify the stages of lung cancer and pursue more accurate findings. © 2024 IEEE.

关键词： Medical imaging

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and Information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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A Novel Approach to Mitigating Traffic Issues through Advanced Congest Detection and Adaptive Signal Control Using AI 4

A Novel Approach to Mitigating Traffic Issues through Advanc...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Priya, G. Vishnu Gowri, B.Shyamala Rajalakshmi, B. Dilli Kumar, L. Janagan, Us Sudhir, Rv Easwari Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349900

This paper presents a novel approach to mitigating traffic issues through the development of an AI- driven intelligent traffic congestion identificationand signal switching system. The proposed system aims to improve traffic flow and reduce congestion by employing advanced congestion detection techniques and adaptive signal control mechanisms. The system utilizes state-of-the-art AI algorithms, such as machine learning and deep learning, to accurately identify congested areas in real-time. By analyzing various traffic parameters, includingtraffic flow, speed, and density, the system can effectively detect and predict congestion patterns. Once a congested area is identified, the system employs adaptive signal control strategies to optimize traffic signal timings and prioritize the flow of traffic in congested zones. This is achieved by dynamically adjusting signal timings based on the current traffic conditions, such as the number of vehicles waiting at each intersection and the expected delay. The system also integrates historicaltraffic data to further enhance congestion predictionaccuracy and signal switching decisions. Experimental results demonstrate the effectiveness of the proposed system in reducing traffic congestion and improving overall traffic performance. The AI-driven intelligent traffic congestion identification and signal switching system presented in this paper offer a promising and innovative solution to address traffic issues. © 2024 IEEE.

关键词： Traffic congestion

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Digital twinning of vertical centrifugal casting

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Concurrent engineering Research and Applications 2024年第1-4期32卷 78-89页

作者： Anadkat, Dhaval Dave, Anjali Sata, Amit Shukla, Minal Jarboui, Slaheddine Department of Mechanical Engineering Marwadi University Rajkot India Department of Mechanical Engineering Lukhdhirji Engineering College Morbi India MGL Group Rajkot India Deparment of Computer Engineering College of Computer Science King Khalid University KSA Abha Saudi Arabia

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation. © The Author(s) 2024.

关键词： Centrifugal casting

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Prediction of Chronic Kidney Disease Using ML Techniques 5

Prediction of Chronic Kidney Disease Using ML Techniques

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Shukla, Anupam Robin, Fabian Narvaria, Harshit Joshi, Anju Computer Science and Engineering Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350383522

The study of chronic kidney disease of human body is now a significant topic of interest in the domain of biosciences because of the emerging cases of the disease. One of the most dangerous illnesses in the modern world is chronic kidney disease, for which an accurate diagnosis method needs to be developed to identify and stop the disease at an early stage. A condition known as chronic kidney disease causes a person's kidneys to malfunction and eventually become damaged. This leads to the condition where the kidney is unable to filter the blood and remove the waste as they do. Now-a-days machine learning algorithms have become a major tool in which the disease gets diagnosed based on the certain values like blood pressure, urine value and many such things. This leads to the early detection of the disease so that the patient can consult his required doctor and with the help of which his disease could be cured. The application and the study of the applied algorithm will help us to detect the disease will be explored in this study. These algorithms' outputs have a higher degree of accuracy, sensitivity, and specificity and can be used to predict the presence of chronic kidney disease in the necessary human being. The findings will demonstrate how technology can aid in the better management of chronic kidney disease and how this can enhance decision support systems for healthcare in general. © 2024 IEEE.

关键词： Decision tree GUI Data-driven detection Smart disease detection Machine learning methodologies SVM

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Early Detection of Parkinson's Disease by Neural Network Models 15

Early Detection of Parkinson's Disease by Neural Network Mod...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Suhail, Mohamed S. Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798331300579

This paper establishes neural network models designed to detect Parkinson's disease (PD) during its initial phases. PD, a prevalent neurodegenerative condition, manifests with gradually reduced movement, tremors, limb stiffness, and alterations in walking patterns, including bent posture, shuffling steps, festination, gait freezing, and falls. Identifying PD early on is crucial for timely implementation of therapeutic measures, reducing morbidity. Nonetheless, accurately recognizing PD, particularly in the early stages, poses a challenge due to the elderly population's increased prevalence of PD and common occurrence of progressive gait slowness in other conditions like joint osteoarthritis or sarcopenia. Hence, creating a dependable and objective method is essential for distinguishing PD gait features from those typical in the elderly. This study aimed to devise neural network models utilizing participants' walking motion data to detect PD. We enrolled 32 drug-naïve PD patients with varying disease severity and 16 age/sex-matched healthy controls, measuring their movements using inertial measurement unit (IMU) sensors. The IMU data facilitated the development of neural network models, achieving an average accuracy of 92.72% in identifying advanced-stage PD during validation processes. Furthermore, the models accurately differentiated early-stage PD patients from normal elderly subjects with a 99.67% accuracy. An independent group of participants, recruited to assess the developed models, validated their success in distinguishing PD-affected individuals from healthy elderly, as well as patients at different severity stages. These findings offer robust evidence supporting early diagnosis and monitoring of disease severity in PD patients. © Grenze Scientific Society, 2024.

关键词： Neurodegenerative diseases

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