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共 65467 条记录，以下是4711-4720 订阅

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A Comprehensive Review of Handwriting-Based ADHD Detection in Children with Autism Spectrum Disorder Using machine learning 5th

A Comprehensive Review of Handwriting-Based ADHD Detection i...

引用

5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Deepa, J. Reny Abesha, S. Sasi Priya, M. Nandhini, K. Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819725496

By employing machine learning techniques to analyze handwriting, this study suggests a unique method of diagnosing Attention-Deficit/Hyperactivity Disorder (ADHD) in children with autism spectrum disorder (ASD). Since ASD and ADHD frequently overlap, it can be difficult to diagnose afflicted people and treat them appropriately. The goal of this project is to create an automated system that can identify signs of ADHD based on handwriting characteristics by utilizing the power of machine learning algorithms. The machine learning models are trained using a dataset of handwriting samples taken from kids with ASD and ADHD diagnoses. A range of feature extraction methods, including stroke length, pressure, and velocity, are used to extract pertinent handwriting properties. A variety of machine learning techniques are used, such as random forests, decision trees, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Smart Aquaculture: machine learning-Based Water Quality Prediction in Kadammakudy's Pokkali Fields, Ernakulam

Smart Aquaculture: Machine Learning-Based Water Quality Pred...

引用

2024 international conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Kp, Shibu Perumal Sankar, S. Toc H Institute of Science & Technology Dept. of Ece Arakkunnam Kerala Emakulam India

ISBN: (纸本)9798350387230

Climate change and pollution pose profound threats to aquatic ecosystems, imperiling the long-term viability of aquaculture. To mitigate these impacts, we developed a novel Real-Time Prediction Model integrating Edge computing and Explainable Artificial Intelligence (XAI) specifically designed for farmer accessibility in Kadamakkudy's Pokkalifields, Ernakulam. Leveraging an ensemble of predictive algorithms, our model accurately forecasts critical water quality parameters. Despite limited daily measurements, timely interventions are enabled, safeguarding both ecological balance and agricultural profitability. By ensuring interpretability and openness, XAI enables farmers to make data-driven, well-informed decisions that balance environmental and economic sustainability. © 2024 IEEE.

关键词： Adversarial machine learning

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Multiple disease prediction using machine learning algorithms 5th

Multiple disease prediction using machine learning algorithm...

引用

5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and computing, ICABEC 2023

作者： Dayal, Parth Sharma, Deepansh Agarwal, Aman Chaudhary, Himanshu Gautam, Ruchita Kumar, Praveen Sharma, Abhishek KIET Group of Institutions Delhi NCR UP Ghaziabad India

ISBN: (纸本)9781032642987

The prediction of the diseases was performed by taking different symptoms as input data from the user. In this paper, we have analyzed the presence of many diseases such as Heart diseases, CKD, Liver disease, and many more using basic body parameters such as blood sugar, heart rate, etc. We have applied the most used classification algorithms such as Logistic Regression, KNN, and Random Forest Classifier to anticipate the illness. Finally, the algorithm with the most accuracy is used to train the model. For the prediction of malaria and pneumonia, we have used Deep learning (CNN) which extracts important information, and patterns from the images, match it against a large dataset it already has, and makes the prediction. All the models have good accuracy and have been combined into a single website for easy access. © 2024 The Author(s).

关键词： Forecasting

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Early Termination for Hyperdimensional computing Using Inferential Statistics 25

Early Termination for Hyperdimensional Computing Using Infer...

引用

30th international conference on Architectural Support for Programming Languages and Operating Systems-ASPLOS

作者： Yi, Pu (Luke) Yang, Yifan Lee, Chae Young Achour, Sara Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798400706981

Hyperdimensional computing (HDC) is a brain-inspired, lightweight computing paradigm that has shown great potential for inference on the edge and on emerging hardware technologies, achieving state-of-the-art accuracy on certain classification tasks. HDC classifiers are inherently error resilient and support early termination of inference to approximate classification results. Practitioners have developed heuristic methods to terminate inference early for individual inputs, reducing the computation of inference at the cost of accuracy. These techniques lack statistical guarantees and may unacceptably degrade classification accuracy or terminate inference later than is needed to obtain an accuracy result. We present Omen, the first dynamic HDC optimizer that uses inferential statistics to terminate inference early while providing accuracy guarantees. To realize Omen, we develop a statistical view of HDC that reframes HD computations as statistical samplingandtesting tasks, enabling the use of statistical tests. We evaluate Omen on 19 benchmark instantiations of four classification tasks. Omen is computationally efficient, delivering up to 7.21-12.18x inference speed-ups over an unoptimized baseline while only incurring a 0.0-0.7% drop in accuracy. Omen outperforms heuristic methods, achieving an additional 0.04-5.85x inference speed-up over the unoptimized baseline compared to heuristic methods while maintaining higher or comparable accuracy.

关键词： program optimization unconventional computing hyperdimensional computing emerging hardware technologies edge machine learning

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Human Detection and Tracking using machine learning Algorithm

Human Detection and Tracking using Machine Learning Algorith...

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2024 international conference on Advances in computing Research on Science Engineering and Technology, ACROSET 2024

作者： Nallasivan, G. Thatikonda, Ramya Geetha, M. Bhuvanesh, A. Ramraj, S. Sreethu, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India University of the Cumberlands Department of Information Technology Williamsburg United States Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

The use of OpenCV and its capabilities for person tracking and detection is suggested by this project. The technique works well for following people in a variety of situations, such as busy places, people who are obscured, and people who are moving. The robustness and precision of the estimated model are illustrated through the use of visuals to visualize tracking data. The suggested approach surpasses existing sophisticated algorithms in terms of Intersection over Union (IoU) and mean Average Precision (mAP), achieving excellent accuracy. Potential applications for the proposed method include robots, human-computer interaction, and surveillance. The limits of the suggested approach are also covered, along with suggestions for further research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

引用

4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep learning machine learning Protein Feature Encoding Protein Protein interaction Protein sequence

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Internet of Things (IoT): Role of machine learning in Power Management and Optimization using Regression Analysis 5

Internet of Things (IoT): Role of Machine Learning in Power ...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Hemant Mamoria, Pushpa Dewangan, Deepak Kumar Kumari, Shivani Chhatrapati Shahu Ji Maharaj University Department of Computer Science & Engineering Kanpur India ABV-IIITM Department of Computer Science & Engineering Gwalior India Rama University Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350383522

This article discusses many aspects of internet of thing related to implementation of energy efficient devices in IoT based network. As everyone knows, implementations of smart automation is possible due to energy efficient devices of IoT network at different levels in data transmission. The purpose of this research is to predict the energy efficiency with the help of machine learning model. Therefore, for this we have implement linear regression method which is basically a supervised machine learning technique to predict and analyzed the different factors which affects the power consumption on a particular time in IoT network. In this article we have chosen a linear regression method to predict the cumulative power needed for any IoT device during data transmission. After careful observations we have found that using ML model is more suitable for predicting the total power requirement for any IoT device in the network rather than going for direct calculation. Apart from this ML model selection, power consumption issues in IoT devices while in use has been discussed systematically and the impact of using different ML techniques is also being discussed. © 2024 IEEE.

关键词： AI IoT ML NFC RFID UI WSN

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Predicting DDoS Attacks: A machine learning Approach using ALDDoS Dataset 15

Predicting DDoS Attacks: A Machine Learning Approach using A...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shookdeb, Shantanu Muduli, Debendra Bhatta, Sandesh Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C V Raman Global University Dept. of computer Science and Eng. Bhubaneswar India C V Raman Global University Dept. of computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) Dataset by Warda (Owner). © 2024 IEEE.

关键词： ALDDos Cyber defense DDoS RandomForest Web-proxy

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Employing a machine learning-Based Approach for Enhanced Big Data Analysis in Supply Chain Management

Employing a Machine Learning-Based Approach for Enhanced Big...

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2024 international conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Pradeepa, M. Sumathi, V. Ponnusamy, Sivakumar Suneetha, G. Singh, Gurjeet Kruthika, P. School Of Computer Science Engineering And Information Systems Vellore Institute Of Technology Tamil Nadu Vellore632 014 India Sri Sai Ram Engineering College Department Of Mathematics West Tambaram Tamil Nadu Chennai44 India Ksr College Of Engineering Department Of Computer Science And Engineering Namakkal TamilNadu Tiruchengode India Koneru Lakshmaiah Education Foundation Department Of Cse Andhra Pradesh Vaddeswaram India Ies College Of Technology Ies University Bhopal Department Of Mechanical Engineering Madhya Pradesh India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350364828

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7%, followed by KNN at 96.7% and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain. © 2024 IEEE.

关键词： Forecasting

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A Smart Irrigation System Powered by IoT and machine learning for Optimal Water Management 4

A Smart Irrigation System Powered by IoT and Machine Learnin...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Dhyani, Ritika Manne, Nikita Garg, Jyoti Motwani, Deepak Shrivastava, Ashok Kumar Sharma, Meenakshi Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Cvr College of Engineering Department of Csit Hyderabad India Ims Engineering College Department of Information Technology Ghaziabad India Amity University Department of Computer Science & Engineering Gwalior India Inderprastha Engineering College Department of Electronics & Communication Engineering Ghaziabad India

ISBN: (纸本)9798350360165

In this innovative quest for more efficient irrigation, we investigate the combined use of sensor technology and machine intelligence to optimise agricultural water usage. We collect precise data on essential variables, including water flow, temperature, humidity, soil moisture, and water level, every 15 days by promptly deploying sensors in the field to monitor them in real-time. We use the vast amount of data available to us to train machine learning models, including Recurrent Neural Networks (RNN), K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), specifically for streams that are anticipated to have a certain distribution. CNN has exceptional precision with a decoding accuracy of 94.5% in visual signal interpretation, surpassing ANN, KNN, and RNN which achieve lower accuracies of 91.1%, 88.7%, and 83.6% respectively. Our investigation uncovers a fluid interaction between sensor data and model training, leading to each model exhibiting unique characteristics. These findings not only showease the accuracy with which machine learning can distribute water, but they also signify a noteworthy progress in resource-efficient and sustainable agriculture. In addition to generating mathematical discoveries, our research establishes the foundation for advanced irrigation systems that smoothly integrate mechanical and natural processes in agricultural fields. © 2024 IEEE.

关键词： Soil moisture

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