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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 IEEE.

关键词： Convolution

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An Epileptic Seizure Detection System based on Mediapipe and Deep Learning 2

An Epileptic Seizure Detection System based on Mediapipe and...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Venkatesh, Abhijit Sajini, S. Srm Institute of Science and Technology Department of Computer Science and Engineering RamapuramBharati Salai Chennai600089 India

ISBN: (纸本)9798350360523

Epilepsy, a neurological disorder characterized by recurrent seizures, poses significant challenges in timely intervention and patient safety, affecting millions of individuals worldwide. Early and accurate detection of seizures is crucial for timely medical intervention and improved patient outcomes. This system holds particular significance for the elderly demographic, wherein prompt assistance is imperative to safeguard their lives. This study integrates MediaPipe Pose Detection alongside Machine Learning and Deep Learning techniques to enable the identification of individuals experiencing generalized epileptic seizures within the confines of their residences. Moreover, hyperparameter tuning has been performed on the machine learning algorithms to find optimal combination of hyperparameters for each algorithm. Experimental results show that highest average accuracy among machine learning models is achieved by SVM Classifier and Gradient Boosting algorithms at 88.08%, while Time series classification implemented using 1DCNN performs at 84.77% accuracy. It is noted that more varied data samples and standardized datasets would likely improve current results. © 2024 IEEE.

关键词： Patient treatment

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Vitamin Deficiency Detection Using Neural Networks 9

Vitamin Deficiency Detection Using Neural Networks

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Krishna Chaithanya, T. Gupta, Jiniya Roobini, M.S. Department of Computer Science and Engineering Chennai600119 India Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350350845

Vitamin deficiency is a pervasive health issue affecting millions globally, often leading to severe health complications if undiagnosed. Various vitamin deficiencies can be identified by identifiable symptoms manifesting in numerous areas across the human body. Blood tests are most commonly used to detect deficiencies. However, blood tests are expensive and have limited availability. The project aims to develop an automated system to detect vitamin deficiencies using Mobilenet, convolutional neural networks (CNN), NasNet-mobile, inception modules, and artificial neural networks (ANN). The program allows users to detect potential vitamin deficiencies without needing blood samples by examining images of the eyes, lips, tongue, and nails. Here we have considered the dataset of abnormalities caused by vitamin deficiencies like glaucoma eyes, bluish nails, and other abnormalities of lips and tongue. We have trained the model on these image datasets, by leveraging image recognition and data analytics, the proposed system offers a quick, efficient, and non-invasive method of detection thereby streamlining the diagnostic process and facilitating early intervention. The aim is to provide an economical, accessible, and efficient clinical screening method. Moreover, the platform furnishes tailored dietary suggestions to tackle pinpointed inadequacies, thus alleviating health hazards linked with insufficient nourishment. By furnishing an easily reachable resource for prompt identification and action, our system aids in tackling a prevalent worldwide health concern. The program functions as a beneficial aid for individuals to combat a widespread global challenge, impacting millions due to inadequate comprehension of nutrition. Serving as a valuable resource, it addresses an international issue, assisting those facing nutritional uncertainties worldwide. © 2024 IEEE.

关键词： Blood

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Play Store App Rating Prediction Using Machine Learning & Deep Learning Regression Techniques 2

Play Store App Rating Prediction Using Machine Learning & De...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Biswal, Bhramara Bar Das, Satya Narayan Sahu, Satyanarayan Banerjee, Shobhan Computer Science and Engineering Gandhi Institute of Engineering and Technology Gunupur India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350308846

The rating & review of an app in the App Store or Play Store plays an essential role for end users to get information about the app based on other people's experiences with that app. The reviews might be context-based, but the ratings give a firsthand impression to the users, simply looking at scores out of five. In this paper, we have performed multivariate analysis on the Google Play Analysis Dataset to determine the critical features and prune off the redundant ones, followed by which we compared the accuracy of various regression models in predicting the ratings of various apps in the dataset. © 2023 IEEE.

关键词： Artificial Neural Network Feature Importance Multivariate Analysis Rating prediction Regression

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Regional Heatwave Prediction Using Deep Learning Models In Bangladesh 2

Regional Heatwave Prediction Using Deep Learning Models In B...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Sultana, Syeda Samia Mumu, Sumaiya Siddiqua Fahim, Rafid Reezwan Neha, Noshin Nawar Mahbub, Md. Shahriar Department of Computer Science and Engineering Ahsanullah University of Science and Technology Tejgaon Industrial Area Dhaka1208 Bangladesh

ISBN: (纸本)9798331508227

With the growing threat of heatwaves in Bangladesh due to climate change, predicting heatwave days has become vital for effective mitigation measures. In this study, a robust dataset of 25 years, acquired through the Bangladesh Meteorological department, is capitalized to explore Long-Short Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN) models for predicting heatwave in four heatwave-prone districts of Bangladesh: Dhaka, Rajshahi, Bogra and Dinajpur. The selected features are maximum temperature, humidity, dew point temperature, pressure, wind speed and sunshine. The dataset was divided into 80% for training, 10% for validation and 10% for testing. The predictive performance of BiLSTM, Conv1D, and LSTM models was evaluated with Accuracy, F1-score, R2 score. Bi-LSTM consistently showed high accuracy, with Dhaka and Rajshahi achieving 96.96% and 97.40%, respectively, and Bogra recorded the highest accuracy at 99.57%. In Dhaka, Bi-LSTM had the highest accuracy at 96.96%, followed by Conv1D at 95.65%, and LSTM at 93.04%. For the F1-score, Bi-LSTM led with 86.27%, and in R2 score, it performed best with 77.40%. These results demonstrate the superior performance of the Bi-LSTM model in predicting heatwave days, making it a beneficial tool for early warning and preparedness efforts. Despite the predominant use of statistical models on Bangladeshi datasets, this study explores the novel application of deep learning models like Bi-LSTM and CNNs for heatwave classification, which offer better identification of complex spatiotemporal patterns and are more resistant to anomalies. ©2024 IEEE.

关键词： Long short-term memory

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Deep Learning Based Traffic Sign Recognition using CNN and TensorFlow 5

Deep Learning Based Traffic Sign Recognition using CNN and T...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gowtham, P.Srinivasa Bhargavi, Maridu Kavyanjali, P. Babu, P.Naga Subhash, K. Vignan's Foundation for Science Technology and Research Department of Computer Science And Engineering Vadlamudi AP Guntur India

ISBN: (纸本)9798331530013

It is very hard to detect small objects. When we are talking about self-driving car, then it is a 'driver less' or autonomous vehicle that functions independently without any human intervention. It makes use of hardware or software that can be used for the movement of vehicles from one destination to another. Even though the revolution of self-driving car is unstoppable, it is facing many challenges. The first challenge that we are facing is Traffic rule implementation. For detection and road traffic signals categorization in street view images: We are creating a deep neural network model that is used for describing road signs seen in images or videos. © 2024 IEEE.

关键词： Traffic signs

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Navigating the Landscape of Ransomware Detection Methods: A Review 3rd

Navigating the Landscape of Ransomware Detection Methods: A ...

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Benito, Nikiema Martial, Ouedraogo Banda, Tamiano Sahana, Subrata Department of Computer Science and Engineering School of Engineering and Technology Sharda University UP Greater Noida201306 India

ISBN: (纸本)9789819719600

The global impact of ransomware on cybersecurity has increased alarmingly in recent years. It is the cause of important financial damage for individuals as well as for corporations. From the early days of computers, this type of malware has never ceased to evolve, finding new ways to evade current anti-ransomware detection. Ransomware is a malicious code or piece of code designed to extort payment from victims. Considerable advances have been done on ransomware detection. Nevertheless, many of the proposed solutions fall short because of the continuous sophistication of ransomware. This paper provides a comprehensive review of the recent evolution of ransomware and a critical analysis of state-of-art research contributions based on well-defined evaluation metrics. Our analysis covers various ransomware detection techniques and contrasts their merits and limitations. This review goal is to be a constructive guide for ransomware-oriented research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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CHIE: Generative MRC Evaluation for in-context QA with Correctness, Helpfulness, Irrelevancy, and Extraneousness Aspects 2

CHIE: Generative MRC Evaluation for in-context QA with Corre...

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2nd Workshop on Generalisation (Benchmarking) in NLP, GenBench 2024

作者： Phatthiyaphaibun, Wannaphong Nonesung, Surapon Limkonchotiwat, Peerat Udomcharoenchaikit, Can Sawatphol, Jitkapat Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand

ISBN: (纸本)9798891761827

The evaluation of generative models in Machine Reading Comprehension (MRC) presents distinct difficulties, as traditional metrics like BLEU, ROUGE, METEOR, Exact Match, and F1 score often struggle to capture the nuanced and diverse responses. While embedding-based metrics such as BERTScore and BARTScore focus on semantic similarity, they still fail to fully address aspects such as recognizing additional helpful information and rewarding contextual faithfulness. Recent advances in large language model (LLM) based metrics offer more fine-grained evaluations, but challenges such as score clustering remain. This paper introduces a multi-aspect evaluation framework, CHIE, incorporating aspects of Correctness, Helpfulness, Irrelevance, and Extraneousness. Our approach, which uses binary categorical values rather than continuous rating scales, aligns well with human judgments, indicating its potential as a comprehensive and effective evaluation method. © 2024 Association for Computational Linguistics.

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