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

作者： Muthukumar, B. Kartheesan, L. Pasupathy, Vijayalakshmi Surendran, R. Rajalakshmi Engineering College Department of Information Technology Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

Heart disease is regarded as one of the most significant health problems dealing by people today. Approximately, cardiovascular disease (CVD) affects around 50 million people. Electrocardiogram (ECG) signals are more vital for identifying and keeping track of individuals with various CVDs. In order to detect different types of arrhythmias, this papers proposed a novel optimization driven deep learning model for cardiac arrhythmia detection, termed as OptiDLCardioNet. To improve and smoothen the ECG signal, a cascaded wavelet augmented Kalman (CWAK) filtering approach is first applied. Next, an Adaptive Position aware Black-winged SqueezeNet (APBWSqueezeNet) model is used for feature extraction. In order to classify the signals for arrhythmia disease diagnosis, the extracted features are input into an Enhanced Dilated Height-Width Axial Attention Convolutional Network (EDilBW-HWAACNet). Moreover, the hyper-parameters of the EDilBW-HWAACNet are adjusted through the application of the Improved Walrus Optimization Algorithm (IWOA). The MIT-BIH arrhythmia database is used to validate the performance of OptiDLCardioNet model. Rendering to the experimental results, the OptiDLCardioNet model is able to achieve high classification accuracy of 99.82%, which is superior to existing methods with fewer significant features. © 2024 IEEE.

关键词： Deep learning

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Diagnosing the Early Stages of Alzheimer’s Disease by Applying the Modified Ant Colony Optimization Technique 3rd

Diagnosing the Early Stages of Alzheimer’s Disease by Apply...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kumari, Rashmi Das, Subhranil Singh, Raghwendra Kishore School of Computer Science Engineering and Technology Bennett University Greater Noida India Dehradun India School of CS and AI SR University Warangal India

ISBN: (纸本)9789819726707

Treating Alzheimer’s Disease (AD) and preventing further degeneration are becoming increasingly important. Doctors who could view many morphological aspects for improved clinical practices would evaluate patients more extensively. Previous studies have shown the value of applying deep learning to T1-weighted MRI images to differentiate AD from Normal Control. This paper proposes the classification of three binary classifications by applying 3D CNN Network. A Modified Ant Colony Optimization (MACO) technique is proposed for optimizing the different weights of the network. The effectiveness of the proposed model is assessed by using a sample of 259 ADNI individuals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Building a neural speech recognizer for quranic recitations

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International Journal of Speech technology 2023年第4期26卷 1131-1151页

作者： Al-Issa, Suhad Al-Ayyoub, Mahmoud Al-Khaleel, Osama Elmitwally, Nouh Department of Computer Engineering Jordan University of Science and Technology Irbid22110 Jordan Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza12613 Egypt

This work is an effort towards building Neural Speech Recognizers system for Quranic recitations that can be effectively used by anyone regardless of their gender and age. Despite having a lot of recitations available online, most of them are recorded by professional male adult reciters, which means that an ASR system trained on such datasets would not work for female/child reciters. We address this gap by adopting a benchmark dataset of audio records of Quranic recitations that consists of recitations by both genders from different ages. Using this dataset, we build several speaker-independent NSR systems based on the DeepSpeech model and use word error rate (WER) for evaluating them. The goal is to show how an NSR system trained and tuned on a dataset of a certain gender would perform on a test set from the other gender. Unfortunately, the number of female recitations in our dataset is rather small while the number of male recitations is much larger. In the first set of experiments, we avoid the imbalance issue between the two genders and down-sample the male part to match the female part. For this small subset of our dataset, the results are interesting with 0.968 WER when the system is trained on male recitations and tested on female recitations. The same system gives 0.406 WER when tested on male recitations. On the other hand, training the system on female recitations and testing it on male recitation gives 0.966 WER while testing it on female recitations gives 0.608 WER. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Statistical tests

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Intelligent Drug Release System Development and Evaluation via Support Vector Machine Algorithm

Intelligent Drug Release System Development and Evaluation v...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Kalyani, G. Jayaraman, Venkatesh Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331533137

This study proposes a unique sensor plus machine learning system designed to enhance patient care and targeted medicine administration, with a specific focus on controlling Cryptococcosis, in the search for more precise and proactive healthcare solutions. This research had contributed to the conceptualization, design and implementation of system which integrates constant sensor data monitoring and machine learning algorithms to anticipate and prepare for any health problems. Over the course of a day, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF) were used to analyse sensor data from 10 patients with Cryptococcosis. The sensor data gathered from these people included information on their body temperature, blood pressure, oxygen saturation, heart rate, blood sugar levels, and ECG measurements. The models executed flawlessly, achieving F1 scores for accuracy, recall, and precision. SVM won with a precision score of 97.6%, followed by ANN with a score of 95.3% and RF with a score of 92.1%. The models' capacity to get rid of misclassifications and provide precise predictions needed for right away action was demonstrated by the confusion matrices that went along with them. This discovery, which might revolutionise how patients are treated for difficult-to-treat illnesses like cryptococcosis, is an encouraging first step in the direction of proactive healthcare management. The sensor and machine learning system offers a patient-centered, data-driven approach that keeps up with the changing landscape of healthcare. ©2024 IEEE.

关键词： Support vector machines

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Machine Learning Methods for the Timely Identification of Autism Spectrum Disorder in Toddlers 4

Machine Learning Methods for the Timely Identification of Au...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jain, Sweta Tripathy, Hrudaya Kumar Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

Autism Spectrum Disorder (ASD) is an evolving ailment that upsets the nervous system, by means of a widespread range of symptoms and severity levels. Early ASD identification is challenging and there is no treatment, early detection and intervention are vital for improving outcomes in individuals with ASD. This research, explored the application of machine learning (ML) techniques, specifically Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting (GB), for detection of ASD using a dataset focused on screening toddlers. The dataset, obtained from Kaggle in the 2018 release, encompasses demographic information, medical history, and screening test results for toddlers. The presented system employs leading edge algorithms to analyze the dataset and build predictive models for ASD detection. SVM is preferred due to its capability to lever multifaceted associations in high-dimensional data, while RF excels in handling diverse feature sets and capturing ***, known for its ensemble learning capabilities, is also employed to boost predictive performance. The assessment of these models is accomplished via bench mark estimation metrics in ML. This approach aims at an effective model for ASD detection in children based on the characteristics of the dataset. Additionally, feature importance analysis is conducted to gain insights into the factors contributing to ASD prediction. The implementation results reveals that SVM algorithm outperforms in the proposed ASD diagnosis. Early identification using ML techniques could assist healthcare professionals and caretakers in timely intervention, ultimately improving the prolonged effects for persons with ASD. © 2024 IEEE.

关键词： Support vector machines

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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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UAV-based edge computing system used in energy-saving offloading method

UAV-based edge computing system used in energy-saving offloa...

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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Abdulazeez, Hussein Amer Shaker, Ahmed Maher School of Communication and Information Engineering Huazhing University of Science and Technology Hubei Wuhan China School of Computer Science Univeirty of Wuhan Technology Wuhan China

This paper proposes an offloading strategy based on unmanned aerial vehicle (UAV) managed computer resources to provide dependable communication and intensive processing power in complicated terrain. In order to reduce compute and offload user terminal energy consumption, a UAV-assisted edge computing model is built, taking into account the computing requirements of user terminals, the delay restrictions of computing jobs, and the energy limits of UAVs. The trajectory can be used to reduce the user terminal's energy usage in the allotted amount of time. The simulation results demonstrate that the suggested approach is appropriate for various channel circumstances, allowing the user terminal to accomplish the work while outperforming existing benchmark systems in terms of energy usage. Optimizing the flight trajectory of the UAV will have a substantial impact when the signal-to-noise ratio SNR=20dB. The results demonstrate that, at T=5s, the UAV's flying trajectory optimization strategy reduces energy consumption by 56.26%. © Published under licence by IOP Publishing Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Learning-based Dynamic 3D Placement of Multiple Aerial Base Stations with Heterogeneous Traffic

Learning-based Dynamic 3D Placement of Multiple Aerial Base ...

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2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023

作者： Hoang, Linh T. Nguyen, Chuyen T. Le, Hoang D. Pham, Anh T. University of Aizu Graduate School of Computer Science and Engineering Aizuwakamatsu Japan Hanoi University of Science and Technology School of Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350316803

Deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (ABSs) has emerged as a promising solution to enable high-quality services in next-generation mobile networks. One key challenge is how to place the UAV-mounted Base Stations (UAV-BSs) dynamically over time in response to changes in network situations. Conventional methods usually utilize location information to place the UAV-BSs at the centroid of all users to maximize the average downlink rate. This approach might become ineffective when the traffic demand is highly heterogeneous among users, causing the spatial distribution of traffic demand not to correlate with the distribution of users. This paper considers a time-evolving multi-user multi-server system with heterogeneous traffic, where a group of UAV-BSs provides different services to ground mobile users. We formulate a long-term maximization problem of the user's time-average download speed with the queue stability constraint under random movements of ground users. An actor-critic framework of deep reinforcement learning is proposed to position multiple UAV-BSs efficiently based on a comprehensive view of the network situation, including spatial statistics of the users, traffic demand, and the network's downlink rate. The framework's core module is a deep neural network (DNN) that obtains the UAV's location and three encoded heatmaps of network statistics to predict the optimal movement for UAV-BSs. Extensive simulations demonstrate the guaranteed convergence and great potential of the proposed method regarding the backlog queue length and the average download speed of users. © 2023 IEEE.

关键词： Base stations

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A CNN model to predict and segment cancerousregions from brain images using UNet and morphological operations

A CNN model to predict and segment cancerousregions from bra...

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2022 IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022

作者： Shiju, Thomas M Y Krishna, Addapalli V N Varghese, Bindiya M Computer Science and Engineering School of Engineering and Technology Bengaluru India Computer Science and Engineering Cochin India

ISBN: (纸本)9781665453615

Medical imaging is a technology and a procedure used to produce images of a patient's internal organs and tissues for use in clinical analysis and medical intervention. A tumor is one of the most lethal brain diseases that has affected and destroyed many lives throughout the universe. The two most popular diagnostics to identify a tumor's presence and localize it for potential therapies are brain Magnetic resonance imaging (MRI) and computer tomography (CT) scans. In order to improve treatment methods and decrease diagnostic errors, it is essential to correctly categorize the type of brain disorder. Planning and quantitative evaluation of establishing the size of the tumor provide a significant problem in the treatment of Malignant neoplasm of the brain. MRI is the best way to diagnose a brain tumor without using ionizing radiation. The operator's proficiency is essential to the performance since manually segmenting the degree of a neurological disorder from 3D MRI volumes takes a long time. Another problem that makes the automated detection of brain tumors and segmentation tough research is the appearance of cancers in specific positions within the brain image without differentiating picture intensities. In this case, an authentic, computer-Aided partitioning technique for neurological disorders is expected to estimate the malignant extent. This study presents a computerized approach for segmenting neurological disorders with deep neural networks based on UNet and object identification with erosion. The proposed method has produced accurate tumor object segmentation according to crossvalidation. © 2022 IEEE.

关键词： Tumors

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UWB Radar Performance Analysis Based on OFDM Pilots

UWB Radar Performance Analysis Based on OFDM Pilots

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2023 IEEE International Conference on Control, Electronics and computer technology, ICCECT 2023

作者： Liu, Jing Tian, Jin Zhang, Shuaishuai Jinling Institute of Technology Engineering School of Networks and Telecommunications Nanjing China Anhui University of Science and Technology College of Computer Science and Engineering Huainan China

ISBN: (纸本)9798350399028

The technology of radar - communications integration can obtain situational awareness and unlimited ability to communicate simultaneously without increasing bandwidth. A single wireless hardware platform with a single signal provides both functions of radar and communication i.e. so as to integrate radar and communication systems into a system. Additionally, the hardware platform is very promising since the high performance is provided in radar and communication. Therefore, integrate radar and communication is an emerging and feasible trend. The paper focuses on UWB radar function applications, and system researching the radar performance based on MB-UWB. MB-UWB signal bandwidth is 528 MHz, which includes 12 pilot subcarriers. This paper analyzes radar detection performance of MB-UWB with 12 subcarriers on maximum unambiguous range and velocity, false alarm rate, misdetection rate and loss power induced by pilot. The numerical analysis shows that the MB-UWB specification is suitable for the corresponding radar application as the impulse UWB. This research lays the foundation for the research of radar technology that combines UWB technology and OFDM technology, and provides a new idea for Integrate Radar and Communication with integrated waveform. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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