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A Novel Analysis and Detection of Autism Spectrum Disorder in Artificial Intelligence Using Hybrid Machine Learning

A Novel Analysis and Detection of Autism Spectrum Disorder i...

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2023 IEEE International Conference on Innovative Data communication Technologies and Application, ICIDCA 2023

作者： Senthil, G.A. Prabha, R. Nithyashri, J. Suganthi, P. Thamarai, I. Sridevi, S. Agni College of Technology Department of Information Technology Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Kattankulathur Dept. of Computing Technologies Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397208

Heart Disease or Cardiovascular Disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age and sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include K-Nearest Neighbour (KNN) and decision tree classifier which is compared to yield the desired and efficient output. © 2023 IEEE.

关键词： Heart

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Packet Transmission using Radio Access Protocol for Intra-Cluster communications in Mobile Ad hoc Networks 1

Packet Transmission using Radio Access Protocol for Intra-Cl...

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1st International Conference on Electrical, Electronics, Information and communication Technologies, ICEEICT 2022

作者： Sasikumar, Reshma Vaman, Dhadesugoor R. Poulose, Sojan Lal Computer Science and Engineering Dept. Kerala Kothamangalam India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram India Prairie View AandM University Texas AandM University System Electrical and Computer Engineering Dept. TX United States

ISBN: (纸本)9781665436472

Mobile Ad hoc Network (MANET) is designed to support flexible and seamless deployment for military and homeland security theatres, where every resource is mobile. The resources include radios and base stations. In this paper, we have considered the MANET architecture in terms of multiple clusters with each cluster containing a set of radios and a base station or a Cluster Head (CH). The number of radios in each cluster is selected based on the tactical operation of MANET for different applications. A radio in one cluster can communicate with a radio in a different cluster. For this to occur, radio in source cluster sends data to its own Cluster Head (intra-cluster communication) and the Cluster Head sends this data to a remote Cluster Head (inter-cluster communication) and ultimately to a remote radio (intra-cluster communication). In this paper, only intra-cluster communication is considered at the IP layer and the Physical Layer, where each radio communicates with the CH. When multiple radios are communicating with the CH, there is a need to support an organized method of sending packets without any contention. This paper presents a novel Radio Access Protocol (RAP) for packet exchanges between radios and the CH. The cluster performance is derived in terms of 'effective packet transmission time' from any radio to CH while executing the access protocol. The performance in terms of delay and throughput are presented for different packet sizes. The results show that for a reasonable size of cluster, RAP achieves the throughput greater than 55 percent with minimal delay of transmission of a packet from the radio to the CH. The performance of RAP considers both the IP layer and the Physical layer and the real time system implementation of RAP is done using MATLAB / Simulink and the results are presented. © 2022 IEEE.

关键词： Mobile ad hoc networks

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FBG Sensor Design and Analysis for Early Detection of Cancer

FBG Sensor Design and Analysis for Early Detection of Cancer

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： S C Sharan H K Dhruva T Mary Anitha Neil Roy Preeta Sharan R Madhwesha Moudgalya Dept. of Computer Science and Engineering The Oxford College of Engineering Bangalore India Dept. of MCA The Oxford College of Engineering Bangalore India Dept. of Computer Science and Engineering Delhi Technological University New Delhi India Dept. of Electronics and Communication Engineering The Oxford College of Engineering Bangalore India Dept. of Science and Humanities The Oxford College of Engineering Bangalore India

Several of the current procedures for detecting cancer, such as mammography, ultrasound, MRI, and biopsy, are either expensive, painful, intrusive, or have limitations in accuracy and sensitivity. As a result, there is a need for a simple, noninvasive, and cost-effective tool for detecting cancer at an early stage. Fiber Bragg grating (FBG) sensors offer a wide range of uses in primary care and biomedical applications for intelligent sensing. Furthermore, fiber-optic FBGs have several benefits that set them apart. The most noteworthy of these applications, of course, are incredibly valuable human health indicators such as blood pressure, heart rate, and body temperature. Temperature and blood pressure vary depending on a person’s physical, involuntary, nervous, and mental state. Therefore, measuring vital parameters, especially temperature can help in the early identification of symptoms of a disease. Research suggests that temperature variation is observed in cancerous cells (breast, cervix, and blood vessels). FBGs can be utilized as thermal sensors to measure temperature changes with great sensitivity and precision. FBG sensors also face some challenges, such as low signal-to-noise ratio, cross-sensitivity, and environmental interference. Therefore, optimizing the Q factor of FBG sensors is crucial for improving their performance and reliability for cancer identification. In the current study, an optimized FBG sensor was designed and simulated. The average Q-factor obtained in 244.26.

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An Implementation of Answer Code Validation Program for Code Writing Problem in Java Programming Learning Assistant system

An Implementation of Answer Code Validation Program for Code...

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11th International Conference on Information and Education Technology, ICIET 2023

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Mon, Khin Thet Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Okayama University Dept. of Information and Communication Systems Okayama Japan Tokyo University of Agriculture and Technology Dept. of Computer and Information Science Tokyo Japan National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665465489

To assist Java programming learning of novice students, we have developed the web-based Java Programming Learning Assistant system (JPLAS). It provides various exercise problems to cultivate code reading and code writing skills to support studies at various levels. The code writing problem (CWP) asks a student to write a source code to pass the given test code where the correctness is verified by running them on JUnit. Previously, we implemented the answer platform for students to solve CWP assignments with the automatic runs. However, the teacher needs to manually run them one by one to verify a lot of source codes from students. In this paper, we implement the answer code validation program that automatically verifies all the source codes for one test code and reports the number of tests that each code passed with the CSV file. For evaluations, we applied the program to a total of 999 source codes from 12-43 students for a total of 45 test codes that were assigned in the Java programming course in Okayama university, including for final examinations. The application results confirm the validity and effectiveness of the proposal. © 2023 IEEE.

关键词： Students

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Joint Power and Channel Allocation in Fading Channels Using Deep Reinforcement Learning

Joint Power and Channel Allocation in Fading Channels Using ...

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Advanced Computing & communication Technologies (ICACCTech), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper investigates joint power and channel allocation under fading communication channels using Deep Reinforcement Learning (DRL). To this end, we design a DRL to allocate dynamic resources with different network condition properly and achieve the spectral efficiency improvement while reducing latency to meet lower bound in cost function as well keeping target fairness. Results show that our DRL-based approach can achieve better spectrum efficiency and latency than traditional algorithms while at the same time, sustaining a higher value of fairness index. In addition, it is observed that the DRL method also offers both adaptability (with fast adaptation to network changes) and robustness in performance but at a higher computational expense. Our results illustrate that the DRL-based technique may serve as a strong, scalable candidate in future wireless systems up to several orders of magnitude better than existing resource allocation schemes.

关键词： Fading channels Wireless communication Lower bound Spectral efficiency Heuristic algorithms Neural networks Deep reinforcement learning Robustness Resource management Next generation networking

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies

An Approach of Fog Computing and Edge Computing for Computin...

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Pioneering Developments in computer Science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Devansh Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems.

关键词： system performance Focusing Minimization Load management Reliability Resource management Dynamical systems Distributed computing Optimization Edge computing

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset

Implementation of Pruned and Quantized Semantic Segmentation...

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Device Intelligence, Computing and communication Technologies, (DICCT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts.

关键词： computer vision Quantization (signal) Image analysis Semantic segmentation Computational modeling Artificial neural networks Real-time systems

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Utilization of MFCC in Conjuction with Elaborated LSTM Architechtures for the Amplification of Lexical Descrimination in Acoustic Milieus Characterized by Pronounced Nocturnal Disturbance

Utilization of MFCC in Conjuction with Elaborated LSTM Archi...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Ramesh Singh Rawat Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

In this pioneering research endeavor, we delved into the intricate realm of speech recognition technology, aiming to surmount the formidable challenges posed by acoustically complex environments, notably those marred by pronounced nocturnal disturbance. Through the synergistic amalgamation of Mel-frequency cepstral coefficients (MFCC) and meticulously crafted Long Short-Term Memory (LSTM) architectures, we charted a transformative path toward the amplification of lexical discrimination. Our multifaceted approach not only enhanced the discernment of subtle linguistic nuances but also showcased unparalleled efficacy in the face of disruptive nocturnal noise. The meticulously calibrated fusion of advanced signal processing techniques and neural network architectures culminated in a novel paradigm, revolutionizing the landscape of speech recognition technology. By pushing the boundaries of computational linguistics, this study not only advances scientific understanding but also paves the way for real-world applications in domains reliant on precise and resilient speech recognition systems.

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Mridul Gupta Devansh Yadav Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life.

关键词： Measurement Deep learning Accuracy Computed tomography Classification algorithms

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Lossless Energy Efficient communication of Temporally Correlated Data using Silent Symbol based communication 20

Lossless Energy Efficient Communication of Temporally Correl...

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20th IEEE International Conference on Mobile Ad Hoc and Smart systems, MASS 2023

作者： Majumder, Pratham Chatterjee, Punyasha Sinha, Koushik Cmr Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Jadavpur University School of Mobile Computing and Communication India Southern Illinois University School of Computing Carbondale United States

ISBN: (纸本)9798350324334

We propose a lossless energy-efficient communication scheme for transmitting temporally correlated data in UAV-IoT systems. Existing techniques based on data compression or prediction may lead to information loss, which is undesirable in data-critical applications. Our approach ensures lossless transmission while achieving energy savings. Our proposed approach consists of two phases. In the first phase, we exploit temporal redundancy in the data by preprocessing it to create an equivalent dataset with a significantly high percentage of zeros. In the second phase, the data is encoded to have a highly asymmetric symbol frequency distribution. The symbol with the highest frequency, called the null symbol, is not transmitted, resulting in substantial transmitter energy savings. Despite this, the original data can be easily reconstructed at the receiving end, ensuring lossless communication. The transceiver employs an ASK-FSK modulation/demodulation scheme for silent-symbol based communication. Simulation results using real-life datasets demonstrate that our scheme achieves transmitter energy savings of 10.82 times and 13.6 times compared to existing energy-efficient techniques based on Neighborhood Correlation Sequence (NCS) and discrete wavelet transform (DWT), respectively. Additionally, the proposed approach reduces communication time by a factor of two, leading to a corresponding reduction in receiver energy. The lossless nature of our approach makes it particularly suitable for data-critical applications, distinguishing it from existing lossy techniques those are used for eliminating data redundancy. © 2023 IEEE.

关键词： Wireless sensor networks

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