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An Efficient Dimensionality Reduction Techniques to Data Data Visualization

An Efficient Dimensionality Reduction Techniques to Data Dat...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Sasikala P. Sakthi K. Agalya U. Vidhya M. Karthik R. Nareshkumar Department of Artificial Intelligence and Data Science CARE College of Engineering Trichy Department of Information Technology SRM Valliammai Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Department Of Information Technology Indra Ganesan College of Engineering Tiruchirappalli Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The aim of this paper, is to gain insights in to dynamics of Kabaddi. This sport has become increasingly popular, by analyzing teams’ performance in national leagues. To do so, the results of each round of matches the participating teams play are used to create a dissimilarity matrix. This matrix is then processed through two algorithms, A visualization of the outcomes of each team was achieved by the utilization of novel dimensionality techniques. This novel approach for dimensionality reduction enables the teams’ performance to be represented in a lower-dimensional manner, facilitating the visualization of complex data connections. The t-SNE algorithm, on the other hand, captures the non-linear relationships in the teams’ performance. This study attempts to discover crucial characteristics that impact team performance and give a greater knowledge of the dynamics of the sport by comparing the findings acquired using a novel dimensionality reduction technique. The data for this study comes from the Kabaddi season that took place during 2017-2018.

关键词： Dimensionality reduction Training Fans Heuristic algorithms Data visualization Games Complexity theory

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Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images

Detection of Retinal Diseases in Retinal Optical Coherence T...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： M. Anand T.M. Sheeba A.S.Anshy Princella Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computer Applications SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Chennai India

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used.

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A Novel Approach to Feature Extraction in MI - Based BCI Systems

A Novel Approach to Feature Extraction in MI - Based BCI Sys...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Arun Kumar S L Anand Anil Kannur Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering BLDEA'S V.P. Dr. P.G. Halakatti College of Engineering and Technology Vijayapura

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Brain-computer interfaces based on motor imagery (MI-BCI) enable communication between the brain and the outside world. It accomplishes this by capturing and examining the EEG data generated when a person visualizes moving a certain limb. Based on this investigation; to enhance categorization precision, we enhance the attribute extraction process. Once the EEG signals have been pre-processed and deconstructed into distinct bands of frequencies, we apply similar spatial algorithms for every sub-band. After that, we use Blind Source Separation (BSS) and spatial filter techniques to extract attributes directly from the sources of the brain instead of only from channels. Ultimately, by choosing the most relevant attributes as inputs for the categorization stage, we distinguish between the envisioned movements. To assess our recommended approach, we utilized data set IVa from the BCI competition III. The outcomes from diverse experiments indicate that the system achieves an average accuracy of 98.8%, with 100% sensitivity across all subjects. These results illustrate that the suggested approach effectively improves MI-based BCI.

关键词： Training Sensitivity Accuracy System performance Spatial filters Electroencephalography Brain-computer interfaces Blind source separation Reliability Standards

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Recognition of Heart Disease Based on Primitive Parameters Using Machine Learning Model

Recognition of Heart Disease Based on Primitive Parameters U...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Jyothirmai Digumarthi V. M. Gayathri R. Pitchai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana State India

Cardiovascular diseases (CVDs) have a significant worldwide impact, affecting millions of victims. The complex nature of these conditions requires modern treatments to address them effectively. In order to forecast certain diseases, machine learning algorithms take into account factors like age, gender, and blood pressure, amongst other characteristics. To enhance the usefulness of these algorithms, it is necessary to carefully choose their characteristics in an ideal manner. Various techniques, in order to identify the necessary traits, many techniques, including Recursive Feature Elimination, the least absolute shrinkage and selection operator, Relief approach, and mutual data, are being applied. Methods from the field of machine learning (ML) such as random forest, logistical regression, and K-nearest neighbours (KNN) are frequently employed in the process of predicting cardiovascular disease (CVD). These approaches have demonstrated beneficial effects and may be used to enhance the health of patients through early diagnosis. This study proposes an integrated approach that blends feature selection approaches with machine learning techniques in order to enhance cardiovascular disease prediction. Several methodologies have been evaluated in order to determine the most effective approach for predicting cardiovascular illnesses.

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Average Coverage Probability for Base-Station-to-UAV Communications Over 6G Multiple Access Wireless Networks

Average Coverage Probability for Base-Station-to-UAV Communi...

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IEEE International Symposium on Information Theory

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&#x0026 M University TX USA Department of Electrical and Computer Engineering Princeton University Princeton USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

While the fifth generation (5G) of wireless networks is currently being rolled out, wireless networks still need further development to meet the requirements of dramatically increasing numbers of users and new applications and the resulting traffic expected in the coming decade and beyond. The sixth generation (6G) of wireless networks is envisioned to respond to this by providing services with massive access, ultra-reliability, low latency, intelligence, and security while maximizing the spectral/energy/cost efficiency. Unmanned aerial vehicles (UAVs) have attracted considerable research attention due to their mobility and ability to line-of-sight (LoS) coverage in areas that suffer from low channel quality. However, how to characterize a UAV’s coverage area is a challenging problem and has not been thoroughly studied. To address this issue, in this paper we investigate the coverage performance of base station (BS) to UAV communications with a number of interfering mobile users. We first establish a Nakagami-m fading channel model for BS-toUAV wireless communications. Then, we derive a closed-form expression for the UAV’s average coverage probability under the scenario of interfering mobile users. Finally, numerical results confirm our derived analytical results and evaluate the UAV’s performance under different scenarios that anticipate 6G wireless networking models.

关键词： 6G mobile communication Fading channels Base stations Closed-form solutions Wireless networks Probability Autonomous aerial vehicles

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IoT Based Soil Moisture Monitoring and Nutrients Suggestion Based on the Soil PH Value

IoT Based Soil Moisture Monitoring and Nutrients Suggestion ...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Prasanna R Saravanan P Jenath M Saravanan G Banu Priya Prathaban Rajesh Kanna R Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computer and Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

The Internet of Things (IoT) has a massive influence on machine-to-machine (M2M) and machine-to-human (M2H) technologies, which in turn have caused major changes in society. Country's ability to provide its citizens with food is largely dependent on its agriculture industry. Farmers continue to lack access to technological innovations and advancements in their industry, which causes them to suffer financial losses so we need soil monitoring system. Many chemical and physical possessions of the soil, such as temperature, moisture, and concentrations of nitrogen, phosphorus, and potassium, have a significant impact on crop yield. In order to preserve soil fertility and productivity, farmers can benefit from the increased awareness of continuous cropping and minimum fertilizer use provided by technology and digital gadgets. In order to help farmers in the future with affordable and equable automated irrigation system, a budget friendly prototype for quantifying soil moisture, temperature, pH level and we plan to measure the soil NPK values using sensors. Our proposed work suggests the farmers what nutrient to be added to get better yield using a microcontroller, sensor in conjunction with GSM technology and IoT for continuous analyses of the soil. It will support the efficient cultivation of the land by encouraging moderate pesticide application and soil fertilization.

关键词： Temperature sensors Temperature measurement Soil moisture Prototypes Pesticides Nutrients Sensors Internet of Things Monitoring Farming

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A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

A Hybrid Blockchain-Edge Architecture for Electronic Health ...

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作者： Guo, Hao Li, Wanxin Nejad, Mark Shen, Chien-Chung Northwestern Polytechnical University Research and Development Institute Shenzhen518057 China Northwestern Polytechnical University School of Software Xi'an710129 China Xi'an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China University of Delaware Department of Civil and Environmental Engineering NewarkDE19716 United States University of Delaware Department of Computer and Information Sciences NewarkDE19716 United States

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients' anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA's signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios' requirements while safeguarding EHR and is robust against unauthorized retrievals. © 2004-2012 IEEE.

关键词： Blockchain

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Task Rescheduling for UAV-Assisted Emergency Communications under Uncertainty

Task Rescheduling for UAV-Assisted Emergency Communications ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qiuji Luan Hongyan Cui Xi Yu Biplab Sikdar State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore

In dynamic and unknown emergency networks, (unmanned aerial vehicle) UAV-assisted task scheduling is an important and efficient technique. However, the inevitable uncertainty makes the predetermined schedule decision unfeasible. In this paper, we investigate the effects of parameter fluctuation and unpredictable edge computing node (ECN) failure. We formulate the task rescheduling problem to effectively deal with uncertainty, and design an asynchronous shuffled frog-leaping with jaya (ASJ) algorithm to minimize the makespan, in which the jaya algorithm asynchronously evolves the memeplexes of the shuffled frog-leaping method. To verify the effectiveness of the proposed ASJ, experiments are conducted to compare it with two comparison algorithms under deterministic and uncertain scenarios. The results demonstrate the superiority of the ASJ in terms of makespan.

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Kareem Ali Malalah Al-sharhanee Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Communication Technical Engineering College of Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the “‘terrestrial’ systems and the Unmanned Aerial Vehicles (UAVs)” collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing “the efficiency and efficacy of data collection… with the assistance of Unmanned Aerial Vehicles (UAVs)” which means this work reveals practical application solutions.

关键词： Wireless sensor networks Trajectory planning Autonomous aerial vehicles Turning Routing Rendering (computer graphics) Sensor systems Trajectory Sensors Vehicle dynamics

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Deep Convolution Network Analysis for Crop Growth Prediction 7

Deep Convolution Network Analysis for Crop Growth Prediction

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Sivakumar, Ramkumar Vasudevan, C.K. Karipp, Sarnaam Mustafa Karthikeyan, Sahaana Sankaranarayanan, Suresh San Jose State University Department of Computer Engineering San Jose United States Deloitte Business Technology Analyst Chennai India Infosys Systems Engineer Bangalore India Tata Consultancy Service Assistant Systems Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9781665421683

Farmers have been facing a lot of difficulties in yielding crops in a healthy state. There has been work where Remote sensing technologies employed for getting real time information towards crop growth. Also work has been done in employing Machine and Deep learning for predicting the stages of apple growth, plant phenological and olive tree phenological stages. But in none of work, there has been an attempt to predict the stage of crop growth with a resource optimised Deep Convolution Neural Network (DCNN) model. So we in this work have performed comparitive analysis of three CNN models like VGG19, Xception and Mobile NetV2 for rice crop growth as case study in terms of accuracy, error and memory usage for resource constrained devices. This could help the farmer in assessing the stages of crop growth to take early action before it drastically affects the entire farm © 2022 IEEE.

关键词： Crops

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