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

作者： Anandaraj, B. Monisha, P. Krishna Vinaya, M. Laya, V. Mamatha, V. Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Annamayya-Dt Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331300579

Parkinson's disease is a common neurological condition that predominantly im-pacts people who are older than fifty, leading to speech impairments and movement diffi-culties. Timely diagnosis of PD is essential for efficient care and improved patient outcomes, especially considering the global aging population. Our paper introduces a new method for the early identification of Parkinson's disease utilizing machine learning techniques and the Xception architecture. Our focus lies on analyzing spiral and wave drawings, commonly used in clinical diagnosis. We curated a dataset comprising such drawings from individuals with and without PD and processed the data for model training. Leveraging the Xception architecture, our machine learning models achieved promising results, with a training accu-racy of 95.34% for spiral drawings and 93.34% for wave drawings, along with validation accuracies of 93.00% and 86.00%, respectively. Our highlight the capabilities of machine learning methodologies and the Xception architecture in improving the precision and accu-racy of the diagnosis of Parkinson's disease. Implementing our proposed approach could significantly advance early detection efforts, thereby enhancing patient care and quality of life. © Grenze Scientific Society, 2024.

关键词： Neurodegenerative diseases

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Advances in AI-based genomic data analysis for cancer survival prediction

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Multimedia Tools and Applications 2025年第14期84卷 14139-14166页

作者： Deepali Goel, Neelam Padmavati Khandnor University Institute of Engineering and Technology Panjab University Chandigarh160014 India Chandigarh160012 India Department of Computer Science Guru Nanak College Budhlada151502 India

Problem: Cancer is one of the deadliest diseases prevalent in the world. Survivability, early diagnosis, and accurate prognosis are of utmost importance for the therapeutics and clinical management of cancer patients. To achieve accurate and timely prediction of the survival of cancer patients, several machine-learning models based on genomic data have been proposed but a comprehensive review of recent applications in cancer survival prediction is lacking. This paper represents a review of the most recent application of machine learning and deep learning on cancer survival prediction, with a particular focus on the use of genomic data. It specifically targets the most prominent cancer types such as breast cancer, glioblastoma, lung cancer, renal cell cancer, and oral cancer. Methods: A systematic review approach is employed to analyze recent studies on machine learning techniques applied to cancer survival prediction. Emphasis is placed on methodologies utilizing genomic data due to its effectiveness in predicting survival outcomes. Results: This review highlights the efficacy of different machine/deep learning-based techniques in predicting survival outcomes for different cancer types with genomic data. It also provides a summary of the contributions made by different research groups, critically examines the associated challenges, and suggests potential areas for further investigation. Conclusion: Machine learning and deep learning techniques, especially those utilizing genomic data, hold significant promise for accurate cancer survival prediction across diverse cancer types. Despite advancements, challenges such as data heterogeneity and model interpretability still persist. Further research is warranted to address these challenges and develop a comprehensive framework for cancer survival prediction applicable to various cancer types. This review lays the foundation for future investigations in the area of cancer research. © The Author(s), under exclusive licence

关键词： Diseases

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Enhanced ML Based Content-Based Image Retrieval for Mobile Devices

Enhanced ML Based Content-Based Image Retrieval for Mobile D...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Meenakshi, K.M. Singh, Kirti Dilip Batta, Priya Chauhan, Rakhi Bhargava, Deepshikha Lohani, Bhanu Prakash Amity School of Engineering & Technology Amity University Greater Noida India Chandigarh University Department of Computer Science & Engineering Mohali India Geeta University Department of Computer Science & Engineering Haryana Panipat India

ISBN: (纸本)9798350379716

Image retrieval systems based on content can be developed for mobile applications that provide users with a seamless and efficient way to search for images based on their content. The development of CBIR systems for mobile applications presents unique challenges and opportunities. Mobile devices typically have limited computational resources, storage capacity, and network bandwidth compared to traditional computing platforms. Therefore, CBIR systems designed for mobile applications must be lightweight, efficient, and optimized to run smoothly on these devices. The Adaboost algorithm progressively learns weak classifiers and aggregates them to construct a robust final classifier. Subsequently, a weak learner is introduced, and the data undergoes reweighting. Misclassified test images are assigned greater importance while correctly classified ones are downweighted. Consequently, subsequent weak learners prioritize the query images previously misclassified by their predecessors. The average precision is increased in further iterations to 95.6% from the initial iteration of 94.9%. Average recall shows a little more improvement from the previous set by increasing from round 1 with 94.1%, and increasing to 95.9% towards the last round of iteration. The F-Measure keeps fairly stabilized at 95.1478 after the first round. The time taken by every iteration reduces slightly from 3. © 2024 IEEE.

关键词： Content based retrieval

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Comparative Analysis of Air Quality Index Using Large Language Models and Machine Learning 4

Comparative Analysis of Air Quality Index Using Large Langua...

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4th International Conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2024

作者： Sundaramurthy, Shanmugam Sai Harish, G. Reddy, Chandra Shekar V. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9781643685519

Accurate air quality prediction is crucial for environmental monitoring and public health. This study explores a novel approach using machine learning algorithms and large language models to predict the Air Quality Index (AQI). While traditional AQI prediction relies on complex models and extensive data on pollutants and meteorological factors, this research investigates the use of readily available fuel consumption data as an alternative predictor, given its close link to emissions. The study employs supervised machine learning algorithms, including Random Forest and Gradient Boosting, utilizing fuel consumption data as input features to build predictive models. Additionally, a state-of-the-art large language model, GPT-3.5-turbo-instruct, is fine-tuned on historical AQI data and evaluated for its predictive capabilities. The performance of both machine learning models and the language model is compared using various metrics, and the results demonstrate that both approaches achieve high AQI prediction accuracy, outperforming traditional methods based on pollutant concentration data. Notably, the fine-tuned language model exhibits superior performance, potentially due to its ability to capture complex dependencies and contextual information from the training *** work highlights the potential of leveraging readily available fuel consumption data and advanced language models for accurate and cost-effective AQI prediction. The findings have significant implications for developing scalable air quality monitoring systems, enabling timely interventions and informed decision-making to mitigate the adverse effects of air pollution. © 2024 The Authors.

关键词： Prediction models

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A Transformer-based approach for Fake News detection using Time Series Analysis 7

A Transformer-based approach for Fake News detection using T...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Ur Rahman, Aniq Chaudhry, Hassan Nazeer Asim, Malik Muhammad Kulsoom, Farzana University of Engineering and Technology Computer Department Taxila Pakistan Barani Institute of Technology Computer Science Department Rawalpindi Pakistan University of Engineering and Technology Telecommunication Engineering Department Taxila Pakistan

ISBN: (纸本)9798350338461

Fake news is a growing problem in the digital age, spreading misinformation and affecting public opinion. Existing fake news detection is based on style analysis or news generator's behavior analysis, the former fails if the content is generated using existing news corpus while the latter requires a very specific data set. In this research, we aim to address the issue of fake news detection by employing deep learning-based time series analysis (TSA). We propose a TSA method to gauge the authenticity of the news based on previously available news content of a similar genre. We employed pre-trained models for encoding including GloVe and BERT and transformer-based sequence-to-sequence (Seq2Seq) for TSA. The results demonstrate 98% accuracy of pre-trained models, such as GloVe and BERT, over traditional encoding approaches having accuracy between 77% and 93%. Our study also compares the effectiveness of various deep learning methods, including Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM with attention, GRU with attention, and Transformers with 8 and 16 multi-heads. The results show that Transformers with 8 and 16 multi-heads achieve 98% and 97% accuracy respectively as compared to LSTM (87%) and GRU (88%). Our work is useful for future research in TSA-based fake news detection and proposes to use GloVe and BERT-based encoding and multi-head transform architecture. © 2023 IEEE.

关键词： Harmonic analysis

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG Signal with Correlation-Based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 IEEE.

关键词： Deep neural networks

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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 39-56页

作者： S.Kalavathi R.Nedunchelian Department of Information Technology Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science&Engineering Excel Engineering CollegeKomarapalayamNamakkal638183India

Network analysis is a promisingﬁeld in the area of network applications as different types of trafﬁc grow enormously and *** route prediction is a challenging task in the Large Scale Networks(LSN).Various non-self-learning and self-learning approaches have been adopted to predict reliable *** protocols decide how to send all the packets from source to the destination addresses across the network through their *** the current era,dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.A novel method named Reliable Route Prediction Model(RRPM)is proposed toﬁnd the best routes in the given hefty gage network to balance the load of the entire network to advance the network *** task is carried out in two *** theﬁrst phase,Network Embedding(NE)based node classiﬁcation is carried *** second phase involves the network analysis to predict the route of the *** experiment is carried out for average data transmission and rerouting time is measured between RRPM and Routing Information Protocol(RIP)protocol models with before and after failure *** was observed that average transmission time for RIP protocol has measured as 18.5 ms and RRPM protocol has measured as 18.2 *** the proposed RRPM model outperforms well than the traditional routeﬁnding protocols such as RIP and Open Shortest Path First(OSPF).

关键词： Network embedding node classiﬁcation link prediction routing protocols novel RRPM

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