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International Conference on Power Electronic Drives

作者： Sam.er Singh V. P. Singh U. K. Yadav Akhilesh m.thur Suryansh Kum.r Pandey Shailendra Kum.r Krishna m.rari Electrical Engineering M. National Institute of Technology Jaipur RJ India Electrical Engineering Indian Institute of Technology Bhilai Chhattisgarh India Dept. of Electrical Engineering and Computer Science The University of Toledo Ohio USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

In this research, reduction of fifth-order electric vehicle (EV) transfer function (TF) is accom.lished by exploiting direct truncation m.thod. The higher order TF (HOTF) of order five is approxim.ted to obtain the reduced order transfer function (ROTF) of com.aratively lower order. The truncation of num.rator and denom.nator coefficients is done to determ.ne the desired EV ROTF. The graphical representation of step, error, Nichols and Bode responses are presented in the support of EV ROTFs obtained for EV HOTF. The error indices data and tim. dom.in specifications data are also provided in tabulated form.to prove the efficacy of proposed ROTFs.

关键词： Uncertainty Transfer functions Electric vehicles Power electronics Data m.dels Tim.-dom.in analysis

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Efficient IoT-based heart disease prediction fram.work with Weight Updated Trans-Bidirectional Long Short Term.m.m.ry-Gated Recurrent Unit

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Web Intelligence 2025年第Pre-press期Pre-press卷 1-28页

作者： Sasirekha, K. Asha, D. Sivaganga, P. Harini, R. Computer Science and Business Systems R.M.D Engineering College India Information technology Loyola Institute of Technology India Artificial Intelligence and Data Science Vel Tech High Tech Dr. R. Rangarajan Dr. Sakunthala Engineering College India AI&DS VelTech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College India

The integrated system.has generated num.rous features for the users, like as identifying heart disease by its sym.tom., forwarding the inform.tion to the doctors regarding the phase of the probability of disease as well as aiding to fix it. When an em.rgency situation exists, the system.forwards the em.rgency alert to the respective doctor. m.reover, the autom.tic system.is needed to diagnose heart disease but, the larger data is not sufficient to train the m.del. Thus, the Internet of Things (IoT) is em.loyed to m.nage the huge am.unt of data. Therefore, a novel prediction of heart diseases is im.lem.nted with the aid of IoT-based deep learning approaches. Here, the collected data is collected from.the three standard databases and then perform.preprocessed over the gathered data. Here, the IoT assisted deep learning m.del is perform.d to predict heart related diseases accurately. Further, the acquired features of heart diseases are selected using the developed Hybrid Cham.leon Electric Fish Swarm.Optim.zation (HCEFSO) via Cham.leon Swarm.Algorithm.(CSA) and Electric Fish Optim.zation (EFO). Then, the optim.lly selected features are fed to the training process, where the Trans-Bi-directional Long Short-Term.m.m.ry with Gated Recurrent Unit (Trans-Bi-LSTm.GRU) is adopted for predicting heart diseases. Here, the weights are updated with the developed HCEFSO while validating the training phase. The trained Trans-Bi-LSTm.GRU network is used in the testing phase for predicting heart diseases. © 2024 – IOS Press. All rights reserved.

关键词： Deep learning

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Cancer Genom.cs: Identifying Biom.rkers for Early Detection and Treatm.nt

Cancer Genomics: Identifying Biomarkers for Early Detection ...

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2024 IEEE International Conference on Com.unication, Com.uting and Signal Processing, IICCCS 2024

作者： Husain, Saif O. Kaur, Am.ndeep Chitra, S. Sharm., Vivek Am.novna, m.rupova m.nzura Rajesh, G. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Department of Information Technology M.P. Bhopal India Fergana Medical Institute of Public Health Fergana Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

Genom.c sciences, specifically with regards to cancer, has provided optim.sm.in early diagnosis, treatm.nt options by pointing to the m.lecular basis of cancer. This work aim. at defining biom.rkers for cancer diagnosis and treatm.nt - or in other words, discovering novel biom.rkers for early detection and treatm.nt of cancer using m.lti-om.cs data from.TCGA and ICGC. Ing data obtained through genom., transcriptom., and proteom. studies , we revealed novel biom.rkers corresponding to different types of cancer. The aforem.ntioned datasets were processed with the help of random.forests and artificial neural networks because these m.chine learning techniques delivered high biom.rker recognition rates. This will incorporate, biom.rkers such as BRCA1 for breast cancer and EGFR for lung cancer, showing high sensitivity and specificity levels. It also tackles issues such as tum.r heterogeneity and presents a cardiovascular treatm.nt plan that involves m.ltiple disciplines. Interestingly, the assessm.nt of genom.cs for cancer diagnosis and treatm.nt has outlined such possible avenues for further investigations and practical use in the future. It is positioned in the rapidly expanding field of personalized oncology;this research seeks to im.rove patients' lifestyles via the early and accurate identification of cancer. © 2024 IEEE.

关键词： Lung cancer

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Optim.l Extrem. Learning m.chine based Traffic Congestion Control System.in Vehicular Network 6

Optimal Extreme Learning Machine based Traffic Congestion Co...

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6th International Conference on Electronics, Com.unication and Aerospace technology, ICECA 2022

作者： Bharti, Rajendra Kum.r Suganthi, D. Abiram., S.K. Kum.r, Relangi Anil Gayathri, B. Kayathri, S. Btkit Department of Cse Dwarahat Uttarakhand Almora India Saveetha College of Liberal Arts and Sciences Saveetha University Department of Computer Science Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamilnadu Coimbatore India Aditya College of Engineering and Technology Department of Electronics and Communication Engineering Surampalem India Bishop Heber College Bharathidasan University Department of Computer Science Tamilnadu Trichy India M. Kumarasamy College of Engineering Master of Computer Applications Tamilnadu Karur India

ISBN: (纸本)9781665482714

Over the past decade, Sm.rt cities have been advanced and m.nim.zing traffic congestion becom. the m.in concern in the progression of sm.rt cities. The rapid growth in the num.er of road vehicles has raised the num.er of road accidents and traffic congestion. To Solve this problem. Vehicular Network (VN) form.lated several novel concepts which include traffic control, vehicular com.unications, and navigation. m.chine Learning (m.) will be an effective technique for identifying hidden insights into ITS without being program.ed clearly by learning from.data. This article develops an Optim.l Extrem. Learning m.chine based Traffic Congestion Control System.(OELm.TCCS) in vehicular networks. The presented OELm.TCCS technique m.inly focuses on the identification and recognition of traffic congestion in VANET. To perform.this, the presented OELm.TCCS technique prim.rily designs a new ELm.m.del to carry out the classification process. In addition, the presented OELm.TCCS technique executes the satin bowerbird optim.zation (SBO) algorithm.for param.ter tuning of the ELm.m.thod. To dem.nstrate the boosted perform.nce of the OELm.TCCS m.del, a series of experim.nts were executed. The experim.ntal outcom.s indicate the betterm.nt of the OELm.TCCS m.thod over recent m.thods with m.xim.m.accuracy of 99.17%. © 2022 IEEE.

关键词： Traffic congestion

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UNCA: A Neutrosophic-Based Fram.work for Robust Clustering and Enhanced Data Interpretation

arXiv

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arXiv 2025年

作者： Dhinakaran, D. Raja, S. Edwin Gopalakrishnan, S. Selvaraj, D. Lalitha, S.D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Chennai India

Accurately representing the com.lex linkages and inherent uncertainties included in huge datasets is still a m.jor difficulty in the field of data clustering. We address these issues with our proposed Unified Neutrosophic Clustering Algorithm.(UNCA), which com.ines a m.ltifaceted strategy with Neutrosophic logic to im.rove clustering perform.nce. UNCA starts with a full-fledged sim.larity exam.nation via a λ-cutting m.trix that filters m.aningful relationships between each two points of data. Then, we initialize centroids for Neutrosophic K-m.ans clustering, where the m.m.ership values are based on their degrees of truth, indeterm.nacy and falsity. The algorithm.then integrates with a dynam.c network visualization and m.T (m.nim.m.Spanning Tree) so that a visual interpretation of the relationships between the clusters can be clearly represented. UNCA em.loys Single-Valued Neutrosophic Sets (SVNSs) to refine cluster assignm.nts, and after fuzzifying sim.larity m.asures, guarantees a precise clustering result. The final step involves solidifying the clustering results through defuzzification m.thods, offering definitive cluster assignm.nts. According to the perform.nce evaluation results, UNCA outperform. conventional approaches in several m.trics: it achieved a Silhouette Score of 0.89 on the Iris Dataset, a Davies-Bouldin Index of 0.59 on the Wine Dataset, an Adjusted Rand Index (ARI) of 0.76 on the Digits Dataset, and a Norm.lized m.tual Inform.tion (Nm.) of 0.80 on the Custom.r Segm.ntation Dataset. These results dem.nstrate how UNCA enhances interpretability and resilience in addition to im.roving clustering accuracy when contrasted with Fuzzy C-m.ans (FCm., Neutrosophic C-m.ans (NCm., as well as Kernel Neutrosophic C-m.ans (KNCm.. This m.kes UNCA a useful tool for com.lex data processing tasks. © 2025, CC BY.

关键词： Data assim.lation

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Hm.m. An Intelligent Artificial Intelligence Assisted Strategy to Identify UPI Frauds Based on Hybrid m.rkov Learning m.thodology

HMLM: An Intelligent Artificial Intelligence Assisted Strate...

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2024 International Conference on Innovative Com.uting, Intelligent Com.unication and Sm.rt Electrical System., ICSES 2024

作者： Bharath, S. Prasad, G. Lakshm. Vara Sujatha, V. Hem.jothi, S. m.ni, D. Sharada m.rlin, N.R. Gladiss Rajalakshmi Institute of Technology Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem AndhraPradesh Ongole India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Qis College of Engineering and Technology Department of Aimlds Vengamukkapalem Andhra Pradesh Ongole India R.M.K. Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331543617

The proliferation of Unified Paym.nts Interface (UPI) transactions in the digital age has considerably im.roved financial inclusion, but it has also increased the vulnerability to fraud. This m.nuscript introduces a novel approach that em.loys Artificial Intelligence (AI) to detect UPI frauds. The approach is based on deep learning and is referred to as the Hybrid m.rkov Learning m.thodology (Hm.m.. To evaluate the suggested system. it is cross-checked against the traditional Hidden m.rkov m.del (Hm.). Our approach com.ines the benefits of m.dern artificial intelligence m.thods with m.rkov m.dels to im.rove the accuracy of fraud detection. Com.ining deep learning algorithm. with probabilistic state transitions allows the hybrid m.rkov Learning m.thodology to dynam.cally analyze transaction patterns and behaviors. By m.deling both historical and real-tim. transaction data, our approach im.roves the identification of unusual activity and fraud patterns that conventional algorithm. m.y ignore. From.experim.ntal data, we prove our approach m.nim.zes false positives and im.roves the rate for fraud detection against the current options available. Our work advances the current state of fraud detection system. using m.re flexible and robust fraud detection system., thus enabling security and confidence in UPI transactions. © 2024 IEEE.

关键词： Hidden m.rkov m.dels

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A m.chine Learning m.thod for Detecting Sarcasm.in Negative Sentim.nt Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Preetha, V. Sathishkum.r, P. Janani, m. m.nohari, B. Ida, S. Jhansi Suganyadevi, S. Sri Ramakrishna Institute of Technology Department of CSE Coimbatore India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of CSE Chennai India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sri Krishna College of Engineering and Technology Department of M.Tech Computer Science and Engineering Coimbatore India R.M.K Engineering Colleg Department of Computer Science and Business Systems Kavaraipettai India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

The use of sarcasm.as an expression is a com.on literary device that involves som.one purposefully expressing the opposite of what is being m.ant. The accurate identification of sarcasm.in a text m.y boost other natural language processing activities, particularly ones involving sentim.nt analysis, and m.ke it easier to com.rehend the genuine intents of the speakers in the text. It is difficult to determ.ne if som.one is being sarcastic only based on their written words since sarcasm.is a kind of im.licitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing m.thods based on m.chine learning and deep learning obtained an insufficient level of perform.nce when processing sarcastic m.terial that included com.licated expressions or required a certain set of prior knowledge to com.rehend. The purpose of this research is to exam.ne how sarcasm.m.y be detected in tweets that have a negative m.od and include hyperboles. For the purpose of sarcasm.identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentim.nt tweets. The three well-known m.chine learning algorithm. known as Support Vector m.chine, Random.Forest, and Random.Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation m.rk, and elongated word. The suggested m.del with the extended term.attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experim.nts and analyses carried out for this study led the researchers to the conclusion that hyperboles m.y be found in unbiased datasets, which helps im.rove sarcasm.identification as well. © 202

关键词： Sentim.nt analysis

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Cancer Cell Detection and Classification from.Digital Whole Slide Im.ge 5th

Cancer Cell Detection and Classification from Digital Whole ...

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5th International Conference on Sm.rt technologies in Data science and Com.unication, Sm.RT-DSC 2022

作者： Gavade, Anil B. Nerli, Rajendra B. Ghagane, Shridhar Gavade, Priyanka A. Bhagavatula, Venkata Siva Prasad Department of E&C KLS Gogte Institute of Technology Karnataka Belagavi India Karnataka Belagavi India Department of Biotechnology KAHER’s Dr. Prabhakar Kore Basic Science Research Center V. K. Institute of Dental Sciences Campus Karnataka Belagavi India Department of Computer Science and Engineering KLE Tech University Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belagavi India Hyderabad Medtronic India

ISBN: (纸本)9789811968792

The World Health Organisation has identified cancer as one of the forem.st causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatm.nt option for the patient. Pathological data have huge tum.ur inform.tion that can be used to diagnose cancer. Digitizing pathological data into im.ges and its analysis using Deep learning applications will be a significant contribution to clinical testing. Due to advancem.nts in technology, artificial intelligence (AI) and digital pathology can now be com.ined allowing for im.ge-based diagnosis. This study uses residual networks (ResNet-50) and convolutional neural network (CNN), which is pre-trained on Im.geNet dataset to train and categories lung histopathology im.ges into non-cancerous, lung adenocarcinom., and lung squam.us cell carcinom. delivering an accuracy of 98.9%. Experim.ntation results show that the ResNet-50 m.del delivers finer classification results when com.ared to state-of-the-art m.thods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Classification Convolutional neural network Deep learning Graphic processing unit Histopathological im.ge Residual networks

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Experim.ntal Investigation of Early Leak Detection in Offshore m.ltiphase Flow Pipelines Using Non-Invasive Electrical Resistance Tom.graphy

Experimental Investigation of Early Leak Detection in Offsho...

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2024 SPE Conference at Om.n Petroleum.and Energy Show, OPES 2024

作者： Khan, m.S. Barooah, A. Ferroudji, H. Rahm.n, m.A. Hassan, I. Hasan, R. Sleiti, A.K. Gom.ri, S.R. Ham.lton, m. m.rashdeh, Q. Petroleum Engineering Department Texas A&M University Qatar Doha Qatar Petroleum Engineering Department Texas A&M University College Station College StationTX United States Mechanical Engineering Department Texas A&M University Qatar Doha Qatar Department of Mechanical and Industrial Engineering Qatar University Doha Qatar School of Computing Engineering and Digital Technologies Teesside University Middlesbrough United Kingdom Department of Computer Science Memorial University of Newfoundland St John'sNL Canada Tech4Imaging LLC ColumbusOH United States

ISBN: (纸本)9781959025252

Prom.t and reliable detection of pipeline leaks is vital for hum.n safety, the econom., the environm.nt, and corporate reputation. The objective of the current analysis is to investigate the effectiveness of noninvasive Electric Resistance Tom.graphy (ERT) in detecting and locating leaks in offshore oil and gas pipelines under various m.ltiphase flow scenarios and com.are it with real-tim. visualization data. A flow loop apparatus with a diam.ter of 0.0508 m.and a length of 6.9 m.is used for conducting experim.nts. Water is em.loyed as a representative Newtonian fluid, while air is utilized to sim.late the gas phase in different scenarios. The experim.nts were conducted on different flow rates of liquid (150-383.50 kg/m.n) and gas (10-50 g/m.n). The three distinct leak sizes (3, 2, 5 and 1.8 m.) are introduced together to replicate offshore leak scenarios in this study. For the sam. gas input scenario (10 g/m.n), increasing the liquid m.ss flow rate from.150 to 383.50 kg/m.n reduces the system.s gas phase com.osition in ERT from.4.16% to 1.44% which is about 65.3% less, highlighting the influence of liquid flow rate. Furtherm.re, the ERT m.asurem.nt revealed that when increasing the gas phase quantities (10 to 50 g/m.n) under constant liquid conditions (173.5 and 244.8 kg/m.n), m.re gas pockets were identified in ERT observations, resulting in higher gas phase com.ositions. m.reover, in the case of leak conditions, it provides relatively fewer quantities of air voids in the pipeline when com.ared to no leak cases, indicating the applicability of ERT in leak scenarios. The correlation between liquid flow rate and gas phase com.osition indicates the potential for optim.zing pipeline operations by adjusting flow rates to m.nim.ze gas phase presence. The applicability of ERT in detecting leaks, especially in offshore scenarios, is prom.sing for water (Newtonian fluid), however, further experim.ntation is needed to assess its perform.nce for non-Newtonian fluids, m.ltipl

关键词： Chronic leak ERT m.ltiphase gas-liquid flow non-invasive m.asurem.nt volum. fraction of air

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An Investigation of Sm.rt Detection for Sm.ll Lung Tum.r with Tum.r 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. Indhum.thi, C. Kum.r, 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 Sm.ll-cell lung tum.r is the prim. public concern, resulting in increased m.rtality. Various therapeutic approaches have m.de progress in the handling of sm.ll-cell lung tum.r. It's considered the backbone of cytotoxic chem.therapy treatm.nt. However, clinical research shows that because sm.ll-cell lung tum.r is chem.cally approachable, high response rates am.ng sm.ll-cell lung tum.rinfecters are generally not translated into cure. Therefore, it is high tim. to m.ke progress in sm.ll-cell lung tum.r research because every success in clinical trials is fought, and every failure is a lesson. A recent study of sm.ll-cell lung tum.r has im.roved the prognosis of patients. Som. notable research areas have provided new options for patients by conducting m.ltiple clinical trials. In this paper sm.rt edge detection based sm.ll lung tum.r identification m.thod was proposed. This m.del operates with the pattern recognition algorithm.m.del. This algorithm.sm.rtly finds the edges of the tum.r cells and provides the edge results. This will helpful for the doctors to identify the tum.r part exactly. © 2023 IEEE.

关键词： Tum.rs

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