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Investigation of Convolutional Neural Network with Motor Imagery-based Brain-computer Interface Decoding 14

Investigation of Convolutional Neural Network with Motor Ima...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Madhan, H.K. Bindushree, V. Dept. of Computer Science and Engineering Navkis College of Engineering Hassan India

In recent years, Brain-computer Interfaces (BCI) have become increasingly popular for assisting individuals with disabilities in restoring communication and mobility. The area of research focuses on utilizing convolutional neural networks (CNNs), specifically deeper architectures with high dropout, to decode brain signals and enable users to interact with the world more naturally, particularly through motor imagination. Motor imagination involves analyzing brain signals to decipher a person's intention to perform a motor task. CNNs are well-suited for BCI decoding due to their ability to learn intricate features from raw EEG data. However, the challenge lies in preventing overfitting in deeper CNN architectures, which have a larger number of parameters. To overcome this, the proposed approach involves a high truncation CNN architecture, a regularization technique that randomly turns off neurons during training to mitigate overfitting. © Grenze Scientific Society, 2023.

关键词： Brain computer interface

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UHI Web Application: Empowering Healthcare with Disease Prediction for Seamless Doctor Consultations 4

UHI Web Application: Empowering Healthcare with Disease Pred...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Vardhan, Nunna Harsha Rajesh, M. Vishal Matcha, V.R.S. Reddy, Sathi Ravi Shankar Chowdary, Verelli Nivas Dept. of Computer Science Engineering Amrita School of Engineering Bangalore India

ISBN: (纸本)9798350305258

The large amount of data generated by hospitals is helping machine learning algorithms to generate accurate solutions. This data includes information about treatment, diagnosis, and medications. This valuable information can be used to create automated solutions. The Indian government is planning to introduce a technology like the United Payment Interface (UPI) in the healthcare sector. This technology is called the United Health Interface (UHI). The UHI aims to consolidate various healthcare services, such as the storage of patients' health records, management of blood banks, provision of hospitals' information, doctors' details, bed availability, medical insurance services, and more. Our goal is to develop a similar system where all these services can be accessed through a single account. The UHI is an advanced technology that has revolutionized the healthcare industry. It offers numerous benefits to both patients and healthcare providers. One of the most significant advantages of UHI is the seamless sharing of patient information across different healthcare providers and institutions. This means that doctors, nurses, and other healthcare professionals can easily access patient data, regardless of their location, and make informed decisions regarding treatment. The online doctor appointment system has also revolutionized healthcare. It offers several advantages for patients and healthcare providers alike. These advantages include convenience, time savings, improved access to healthcare, reduced costs, and enhanced patient management. Therefore, our aim is to develop a web application that consolidates all healthcare services under one platform, significantly improving our lives. Here are some additional details about the UHI and the online doctor appointment system, The UHI is a network of open protocols that enable the interoperability of health services. This means that patients' health records can be shared securely and easily between different healthcare provi

关键词： Disease prediction Gradient Boosting Classifier Random Forest Symptoms Unified health interface

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Enhancing Spam Email Detection with a Soft Voting Ensemble of Optimized Machine Learning

Enhancing Spam Email Detection with a Soft Voting Ensemble o...

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

作者： Tazwar, Asif Daiyan, Md Mahir Jiabul Hoque, Md Saifuddin, Mohammed Khaliluzzaman, Md. International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh Fse International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Spam email detection is crucial for cybersecurity, as it protects user privacy and reduces security risks. The persistent presence of spammers necessitates continuous improvements in spam filtering measures. To address this challenge, this study employs Grid Search Optimizer to fine-tune the parameters of four distinct classifiers: Support Vector Machine (SVM), Random Forest, Naive Bayes, and XGBoost. Soft Voting serves as the final classifier, enhancing overall accuracy by integrating results from optimized classifiers to detect and classify spam emails across the Spam Mails Dataset and Enron1 Dataset. The experimental study compared the proposed model with relevant works and found that hyperparameter tuning and Soft Voting significantly enhanced its performance compared to existing approaches. The proposed ensemble model outperformed individual classifiers, achieving accuracies of 99.32% and 99.12%, respectively. Also, the ensemble model demonstrated an AUC of 1.00 on both datasets, indicating its effectiveness in distinguishing between spam and ham emails. This approach surpasses prior studies in accuracy, generalization, and robustness by innovatively combining Grid Search and Soft Voting. The constructed model exhibits high effectiveness and efficiency in the detection of spam emails. © 2024 IEEE.

关键词： Decision trees

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Leveraging ReRAM Sequence Graphs for Efficient Mapping of Binary Adders in ReRAM Crossbars 28

Leveraging ReRAM Sequence Graphs for Efficient Mapping of Bi...

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28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Dhal, Pragnya Deb, Arighna Das, Subrata Das, Debesh Kumar Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Supreme Knowledge Foundation Group of Institutions Dept. of Computer Science & Engineering Hooghly India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350380101

Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles. © 2024 IEEE.

关键词： Data transfer

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Estimating Surface Soil Moisture based on Remote Sensing Data and Crop Yield Classification using Machine Learning 9

Estimating Surface Soil Moisture based on Remote Sensing Dat...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Anuradha, G. Susrith, Maddi Siddhardha, Kondreddy Gnana Velagapudi Ramakrishna Siddhartha Engineering College Dept. of computer science and engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350377972

Soil moisture plays a critical role in environmental monitoring and sustainable agricultural practices due to its significant impact on crop growth, water availability, and overall ecosystem health. Properly understanding and managing soil moisture is vital for efficient water usage, especially in agriculture. Traditional methods in measuring soil moisture have been confined to ground-based small-scale methods, which are high price due to high labor intensity and limited spatial coverage. This project addresses its limitation in the spatial view by integrating remote sensing data from the Sentinel-1 satellite. Sentinel-1 is particularly effective in monitoring the soil moisture as it captures data in all climate conditions. By combining the satellite data with Google Earth Engine (GEE) we create a soil moisture dataset. For analyzing the joint satellite and ground truth data, the machine learning technique using Random Forest (RF) is applied and classification of crop yield is done. © 2024 IEEE.

关键词： Soil moisture

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Balance Relation-Aware Attention Embedding Model for Knowledge Graph Completion 6

Balance Relation-Aware Attention Embedding Model for Knowled...

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6th IEEE Pune Section International Conference, PuneCon 2023

作者： Khobragade, Anish Ghumbre, Shashikant Pachghare, Vinod Coep Technological University Dept. of Computer Science and Engineering Pune India Government College of Engineering and Research Avasari Khurd Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350324204

Predicting missing links has great significance in knowledge graph completion because it helps improve the accuracy and completeness of the information in the graph. The recent models employ an attention mechanism to capture the graph network. Previous attention-based models treat similar relation semantics for associated pairs of entities, but relations might have similar names but represent different semantics. Thus, models fail to capture the semantic dissimilarity of relations involved within the triplet entity pair. This paper proposes a new graph attention model capable of effectively learning embeddings by combining relationship features with triplet entity embeddings to balance relationship contributions while capturing meaningful graph structure. The model incorporates relational compositional embeddings to determine distinct attention for neighboring nodes during computation and update the entity embedding. This novel approach outperforms the previously established models on the benchmark dataset. © 2023 IEEE.

关键词： Knowledge graph

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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation 2

Blockchain Based Secure and Decentralized Smart Licensing of...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Haque, Momenul Paul, Subrata Kumer Islam, Kamrul Della, Mursheda Nusrat Paul, Rakhi Rani Fahim, Sultan Bangladesh Army University of Engineering and Technology Dept. of Computer Science and Engineering 6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Bangladesh Army University of Engineering and Technology Dept. of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350358667

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city. © 2023 IEEE.

关键词： Smart contract

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection. © 2023 IEEE.

关键词： Image enhancement

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Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing 5

Detection of Bullying Text: A Multi-faceted Approach using M...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumar, Kotyada Mohan Kiran Ullas, Karthik Reddy, Vivek Srikar Snehitha, Munaga Sai Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Computer Engineering Bengaluru India

ISBN: (纸本)9798331504403

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message's context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction and detection of nutrition deficiency using machine learning

Prediction and detection of nutrition deficiency using machi...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Kumar Mishra, Amit Tripathi, Neha Gupta, Ashish Upadhyay, Deepak Kumar Pandey, Neeraj Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Dept. of Computer Science and Engineering Dehradun India Tula's Institute Dept. of Computer Science and Engineering Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9781665474924

The plant producers have a hard time identifying nutritional inadequacies in their crops. The capacity to recognize these comprehensive nutritional deficiencies could help regulate crops properly. Using image processing, Convolutional Neural Network (CNN), the researchers were able to categorize and identify complete nutritional deficiencies in various cultivars. The prototypes would provide prescribed plant fertilizers once nutrient insufficiency was recognized. Iron (Fe), magnesium (Mg), potassium (K), nitrogen (N), calcium (Ca) and complete nutrition were examined. For classifying the image processing techniques were used to turn the images into grayscale & binary data. Using identification and prediction, CNN predicts complete nutritional deficiencies in the plant. CNN high accuracy of detection and diagnosis of nutrient deficits in different cultivars, according to the results compared with Artificial Neural Network (ANN) and DenseNet-121. The design has been tested and the results demonstrate a better way to classify and diagnose complete nutritional deficits in different cultivars. © 2023 IEEE.

关键词： Convolution

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