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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Krishna, Penugonda Seetha Rama Nagarajan, S. Annamalai University Dept. of Computer Science Engineering Tamilnadu Chidambaram India Government College of Engineering Srirangam Department of Computer Science and Engineering Trichy620012 India

ISBN: (纸本)9798350386578

Paddy farming plays a critical role in global food security, yet diseases like Bacterial Leaf Blight (BLB) pose significant threats to crop yield and quality. Early detection and accurate diagnosis are essential for effective disease management. This research study investigates machine learning methods for feature extraction and prediction of BLB in paddy fields. Utilizing a comprehensive dataset of paddy leaf images, several machine learning models including Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbours (kNN), and Gradient Boosting Machine (GBM) were trained and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were employed to assess the effectiveness of each model. The comparative analysis discusses the strengths and limitations of each method, focusing on those that demonstrate superior capability in early and accurate detection of BLB. These findings provide valuable insights for agricultural scientists and practitioners to enhance disease management strategies using modern machine learning techniques. By leveraging this knowledge, specialists can optimize interventions and improve agricultural practices to mitigate the impact of diseases like BLB on paddy farming and ensure global food security. © 2024 IEEE.

关键词： Adversarial machine learning

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Flood Prediction System with Voting Classifier 2

Flood Prediction System with Voting Classifier

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Akshitha, K. Vijay Anand, M. Saveetha Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372847

Flood prediction plays a vital role in reducing the influence of natural disasters among the communities and the infrastructure. This paper presents a machine learning approach for flood prediction using a voting classifier, combining information from turbidity, gauge height, and discharge as key features. The dataset, comprising historical records of these parameters, serves as the foundation for training and evaluating the predictive models. In our work, we employ a voting classifier, integrating Random Forest and Gradient Boosting algorithms to harness the strengths of diverse models. The ensemble approach aims to enhance prediction accuracy and robustness by aggregating the predictions of individual classifiers. The choice of features turbidity, gauge height, and discharge is motivated by their relevance in capturing hydrological dynamics associated with flood events. The dataset is preprocessed to handle missing values, normalize features, and encode the target variable. The voting classifier is being trained upon a subset of data, and its accomplishment is estimated on a discrete test set. Various metrics, including accuracy, are employed to assess the model's predictive capabilities. Results indicate promising performance in flood prediction, showcasing the potential of ensemble methods for such applications. This system assures an accuracy of about approx. 99% and alert notification will be sent to the residents of flood predicted areas. Hence, this contributes to the ongoing efforts in leveraging machine learning techniques and also provides an alert notification for enhancing disaster preparedness and response with proper communication. © 2024 IEEE.

关键词： Turbidity

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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Statistical Analysis of the Properties of Geometric Network with Node Mobility 29

Statistical Analysis of the Properties of Geometric Network ...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Arquam, Md. Tiwari, Utkarsh Kumari, Suchi Iiit Dept. Computer Science and Engineering Haryana Sonepat India Shiv Nadar Institute of Eminence Dept. Computer Science and Engineering Delhi-NCR India

ISBN: (纸本)9798331517816

The movement changes the underlying spatial representation of the participated mobile objects or nodes. In real-world scenarios, such mobile nodes can be part of any biological network, transportation network, social network, human interaction, etc. The change in the geometry leads to the change in various desirable properties of real-world networks, especially in human interaction networks. In real life, human movement is concerned with a better lifestyle where they form their new connections due to the geographical changes. Therefore, in this paper, we design a model for geometric networks with mobile nodes (GNMN) and conduct a comprehensive statistical analysis of their properties. We analyze the effect of node mobility by evaluating key network metrics such as connectivity, node degree distribution, second-hop neighbors, and centrality measures. Through extensive simulations, we observe significant variations in the behavior of geometric networks with mobile nodes. © 2024 IEEE.

关键词： Complex networks

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Design and Development of Cognitive Training Systems Based on Extended Reality and BCI Technology 7

Design and Development of Cognitive Training Systems Based o...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Lin, Pei-Jung Ku, Hsiu-Chien Lin, Li-Lun Feng Chia University Dept. of Information Engineering and Computer Science Taichung Taiwan Hungkuang University Dept. of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350307207

Dementia is considered a Mild Cognitive Impairment (MCI), representing a blurred transition from normal cognitive function to dementia. MCI might be a precursor to dementia being caused by various neurodegenerative diseases, including Alzheimer's disease, stroke, metabolic disorders, and mental health conditions. According to the World Health Organization (WHO), over 55 million people worldwide are suffering from dementia, with nearly 10 million new cases each year. This number is expected to rise to 78 million by 2030. Numerous studies have proposed various training methods targeting specific cognitive domains in dementia. For instance, reminiscence therapy encourages individuals to recall buried memories, while reality orientation therapy focuses on strengthening orientation cues to help patients with dementia establish a stronger sense of control. With aging, changes occur in cognitive and physical functions. Research suggests that memory functions can be improved through training. Therefore, the primary goal of this study was to develop an Extended Reality (XR) cognitive system to assist MCI patients in enhancing logical thinking, increasing independence in daily life, and improving response accuracy. The application of extended reality technology in the medical field is expanding, covering areas such as surgical assistance, medical consultation, disease treatment, and medical training. We integrated mixed reality devices with brain-computer interface (BCI) equipment. By detecting brainwave signals in the frontal region of the brain through sensors, neural activity was quantified, and digital brainwave data was transmitted to the system. This data was then visualized on screen in real-time to analyze changes in brainwave data during training. Through multifaceted task training, the system enhanced cognitive response and sensory perception, stimulating brain activity with the goal of strengthening cognitive functions. © 2024 IEEE.

关键词： Mixed reality

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AI-Driven Dynamic Network Slicing for Resource Optimization in 5G Networks: Implementation and Performance Evaluation

AI-Driven Dynamic Network Slicing for Resource Optimization ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Jain, Ayushi Jain, Prateek Verma, Aditya Gopal, Lisa Chaudhary, Preeti Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Scholar Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

We introduce in this paper an AI-based dynamic network slicing model for resource allocation targeting 5G networks. It does this by using machine learning algorithms that help the model learn and adapt to live traffic conditions so as to intelligently manage different service types, including eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communication) and mMTC (massive Machine Type Communication). This leads to substantial improvements in three of the four core performance metrics, latency, throughput and energy efficiency (at a low sacrifice of when it comes to QoS adherence). Their work also demonstrates that AI technologies hold the promise for revolutionary network management, offering a scalable solution that can address ever-increasing requirements of next-generation telecommunications, by surpassing the capabilities of well-established static slicing techniques. © 2024 IEEE.

关键词： 5G mobile communication systems

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images 26

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ratul, Md. Toyaha Rahman Afroge, Shyla Peyal, Hasibul Islam Zafrin, Dia Ahmed, Kazi Fardeen Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach. © 2023 IEEE.

关键词： CNN deep learning fundus image glaucoma image classification

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An Interpretable Framework for Predicting Type 2 Diabetes using ML and Explainable AI 26

An Interpretable Framework for Predicting Type 2 Diabetes us...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Kazi Fardeen Uz Zaman, Md Shahid Peyal, Hasibul Islam Hossain, Anika Rahman Ratul, Md. Toyaha Abdal, Md. Nazmul Islam, Md. Injamul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

Diabetes Mellitus is an incurable disease and stands as a major universe cause of destruction. With the universal prevalence of diabetes increasing rapidly, accurate detection and identification of the disease have become crucial. In this paper, we present an explainable AI-based diabetes indicative methodology that is carefully constructed, effective, and, most importantly, interpretable. Using two real-world diabetes datasets, the three most well-known machine learning classifiers in the literature - Random Forest (RF), Decision Tree (DT), and XGBoost - were subjected with quantitative assessments. After training and assessment of all classification models, the suggested method achieved the best results in the XGBoost classifier for the Sylhet dataset with 99.4% accuracy and the Pima Indian dataset with 92.59% accuracy. The datasets were normalized across observations to provide standardized values for improved analytical applicability and comparability. Explainable AI has been implemented for the XGBoost machine learning model by generating both global and local explanations using Shapley additive explanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The elements that contribute to diabetes are explained and shown in graphs to help medical professionals make decisions about clinical diagnosis and treatment options. © 2023 IEEE.

关键词： Decision Tree (DT) Diabetes diabetes diagnosis explainable AI interpretable LIME Pima Indian dataset Random Forest (RF) SHAP Sylhet dataset XGBoost

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QRAM: Quantum Technology for Random Access Memory 26

QRAM: Quantum Technology for Random Access Memory

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tabassum, Tamanna Akter, Fatema Ahsanullah University of Science and Technology Dept of Computer Science and Engineering Bangladesh Green University Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

The domain of computing, commonly referred to as quantum computing, is dedicated to the development of computer technology that is based on the fundamental principles of quantum theory, elucidates the behavior of matter and energy on the atomic and subatomic scales. In the realm of quantum computing, quantum memory serves as the quantum-mechanical equivalent of conventional computer memory. Quantum memory plays a pivotal role in the development of several quantum information processing devices, including but not limited to a synchronization tool that enables the matching of multiple procedures in a quantum computer, a quantum gate that preserves the identity of any given state, and a method for transforming preset photons into on-demand photons. To take advantage of the fundamental capabilities of quantum computing to perform parallel calculations at incredibly high speeds, a Quantum-based Random Access Memory (QRAM) architecture is proposed herein. This architecture utilizes basic quantum CNOT, V, and V+ gate operations to enable the implementation of basic quantum AND, OR, NOT, and NOR operations, respectively. © 2023 IEEE.

关键词： Ancilla bit QRAM Architecture Quantum RAM Cell

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