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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Kathiravan, P. Shanmugavadivu, P. Saranya, R. Central University of Tami Nadu Department of Computer Science Thiruvarur India The Gandhigram Rural Institute Dept. of Computer Science and Applications India

ISBN: (纸本)9798350399646

The K-means clustering technique is widely used in many fields, such as anomaly detection, customer segmentation, cyber-physical system, medical diagnoses, sentiment analysis, fraud detection, and other similar tasks. We used this k-means technique in handling imbalanced datasets by preserving minority class structure using the stratified resampling technique. For this experimental study, we used a benchmark dataset from Kaggle. It is a labeled dataset collected from online social media regarding fake news. This proposed model, The Stratified k-means Sampling (SKMS), is compared with Synthetic Minority Oversampling Technique (SMOTE) by empirically experimenting using different machine learning algorithms. Random Forest (RF) algorithm gives significant accuracy, and Support Vector Classification (SVC) produces a better F1-score than other algorithms. The SMOTE technique was compared with the same dataset using these same algorithms. While SKMS seeks to preserve the structure of the minority class, SMOTE aims to diversify the minority class by interpolating between existing samples. Depending on the dataset, one might be more relevant than the other. © 2023 IEEE.

关键词： Classification (of information)

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Multi-objective Fuzzy Reliability Redundancy Allocation for xj -out-of- mj System Using Fuzzy Rank-Based Multi-objective PSO 9th

Multi-objective Fuzzy Reliability Redundancy Allocation for ...

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9th International Symposium on Applied Computing for Software and Smart systems, ACSS 2022

作者： De, Satyajit Roy, Pratik Chowdhury, Anil Bikash Department of Computer Science Maheshtala College WB Maheshtala700141 India Department of Computer Engineering and Applications GLA University UP Mathura281406 India Department of Computer Applications Techno India University WB Kolkata700091 India

ISBN: (纸本)9789811967900

The main goal of this paper is to solve a fuzzy multi-objective reliability redundancy allocation problem (MORRAP) for xj -out-of- mj series-parallel system. We consider that system reliability and system cost are two conflicting objectives. Due to the incompleteness and uncertainty of input information, we formulate the objectives by considering the reliability and cost of each component as a triangular fuzzy number (TFN). Here, the fuzzy multi-objective optimization problem of system reliability and cost is analyzed simultaneously using our proposed fuzzy rank-based multi-objective particle swarm optimization (FRMOPSO) algorithm. Comparing the results of FRMOPSO with standard particle swarm optimization (PSO), we see that better optimum reliability and cost have been achieved in the FRMOPSO technique. To illustrate the effectiveness of our proposed technique, we consider the problem of the over-speed protection system of gas turbines containing two mutually conflicting reliability and cost objectives with entropy and several other constraints. We present graphically the effect of optimum system reliability and cost with respect to the percentage change of different parameters. We also compare the convergence rate of FRMOPSO with PSO. Our proposed algorithm shows better results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Redundancy

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A Reliable Approach for Automatic Grading of Diabetic Retinopathy from Fundus Images using Feature Extraction and Fusion 3

A Reliable Approach for Automatic Grading of Diabetic Retino...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Jasmine, R.Anita Ramla, M. Ramesh, M. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science Chennai Kattankulathur India

ISBN: (纸本)9798350369083

Diabetic Retinopathy (DR) is widely recognized as the primary cause of visual impairment worldwide. Early intervention is crucial in preventing irreversible vision loss. Ophthalmologists conventionally utilize fundus cameras to capture retinal images and detect indications of diabetic retinopathy, thereby facilitating prompt diagnosis. The progress made in computational intelligence has significantly enhanced our ability to identify, predict, and categorize DR. The focus of this research is to present a proficient framework for the DR grading. The framework proposed employs Laplacian filters to reduce undesirable image distortions and enhance the overall quality of the images. The methodology involves the utilization of transfer learning through the pre-existing VGG19 Convolutional Neural Network architecture to pull out features from diverse pooling layers. The amalgamation of extracted features from multiple layers results in the formation of feature vectors. These vectors are utilized as inputs for training various machine learning algorithms, with the objective of identifying the most effective model for evaluating the severity of DR. Significantly, the research provides a rationale for its forecasts, guaranteeing the transparency of the algorithm and comprehensibility of the model, thereby validating its reliability. The model's exceptional precision renders it a promising candidate for broad implementation in clinical identification and prediction of DR, potentially augmenting global patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Human Gesture Recognition: Bridging Communication Gaps Through Dynamic Signal Analysis 3rd

Exploring Human Gesture Recognition: Bridging Communication ...

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (纸本)9789819751457

The ability of humans to communicate more effectively via the use of gestures is widely recognised as one of the most important aspects of human interaction. Because of the inextricable link that exists between body language and successful communication, there has been a significant amount of study conducted in the domains of computer vision and human–computer interaction (HCI). Signal recognition is one of the most important aspects of this study. Signal recognition attempts to analyse and decode the information that is transmitted by human motions. These movements include gestures such as hand and facial expressions. Signal recognition is a key bridge that enables people with varying skills to successfully communicate and connect with others. This is particularly true for those who have trouble understanding speech. There is a possibility that algorithms developed for signal detection can help bridge the communication gap and make interactions more welcoming to all parties involved. These attempts constitute a big step forward in the direction of making interactions with computers seem more natural and more analogous to communication between humans. Static signals and dynamic signals are the two basic types that may be distinguished within the field of signal recognition. These critical frames act as turning points, summing together the most important aspects of the meaning sent by the signal. The purpose of this study is to investigate the area of dynamic signal identification and key frame extraction in order to shed light on the advantages and disadvantages associated with both of these processes. By investigating these facets, our ultimate goal is to get a more profound comprehension of the complexities involved in interpreting dynamic gestures and the possible uses of these gestures. This understanding will add to the continuing attempts to make interactions between humans and computers more natural, inclusive, and human-like, which will eventually improve th

关键词： Gesture recognition

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Alzheimer's Disease Classification using Capsule Network 4

Alzheimer's Disease Classification using Capsule Network

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4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Pattanayak, Binod Kumar Mansingh, Padmini Pati, Bibudhendu Dash, Bibhuti Bhusan Gourisaria, Mahendra Kumar Patra, Sudhansu Shekhar ITER SOA Deemed to be University Department of Computer Science and Engineering Bhubaneswar India Rama Devi Women's University Department of Computer Science Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9798350385793

The most prevalent type of dementia is Alzheimer's disease, which impairs thinking, memory, and communication abilities. Better patient treatment is made possible with early disease identification. To detect and predict Alzheimer's disease through the analysis of Magnetic Resonance Images (MRIs), deep learning and big data analytics have recently been created. To improve the classification of Alzheimer's disease patients, this work utilizes capsule network. A critical evaluation is carried out on patients with Alzheimer's disease to improve stage-specific classification. Specifically, the study analyses various methods for performing abnormality identification in brain MRI images and pinpoint the shortcomings of those methods. Thus, it is possible to close the research gaps and enhance the shortcomings of different approaches to produce more accurate and superior results. © 2024 IEEE.

关键词： Convolutional neural networks

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TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil 8

TAMIL- NLP: Roles and Impact of Machine Learning and Deep Le...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Gokila, S. Rajeswari, S. Deepa, S. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Shrimathi DevkunvarNanalal Bhatt Vaishnav College for Women Pg Department of Computer Science Chennai India Christ University Department of Computer Science Bangalore India

ISBN: (纸本)9798350347791

Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes. © 2023 IEEE.

关键词： Learning algorithms

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Identification of Tuberculosis Bacilli from Bright Field Microscopic Sputum Smear Images using U-Net and Random Forest Classification Algorithm

Identification of Tuberculosis Bacilli from Bright Field Mic...

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2023 International Conference on Advances in Intelligent Computing and applications, AICAPS 2023

作者： Greeshma, K. Vishnukumar, S. Cochin University of Science and Technology Department of Computer Applications Kerala Kochi India Union Christian College Aluva Department of Computer Science Kerala Kochi India

ISBN: (纸本)9798350333817

Tuberculosis (TB) is an infectious illness that may be severe and primarily impacts the lungs. Examining sputum smears under bright field microscopes is one of the simplest and most successful ways to detect TB infection in impoverished nations like India. A method for detecting tuberculosis bacteria from bright-field microscopic sputum smear images is proposed in this work. U-shaped encoder-decoder network architecture (U-Net) is used to first segment the bright field microscopic sputum smear images, and then Random Forest Classification Algorithm is used for final prediction. The detection of bacilli produced results that are comparable to other methods. © 2023 IEEE.

关键词： Image classification

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YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness

YOLOv8: A Novel Object Detection Algorithm with Enhanced Per...

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2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Varghese, Rejin Sambath, M. Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India School Of Computing Science Hindustan Institute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for their speed and accuracy in real-time applications. This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness. Inspired by the evolution of YOLO architectures from YOLOv1 to YOLOv7, as well as insights from comparative analyses of models like YOLOv5 and YOLOv6, YOLOv8 incorporates key innovations to achieve optimal speed and accuracy. Leveraging attention mechanisms and dynamic convolution, YOLOv8 introduces improvements specifically tailored for small object detection, addressing challenges highlighted in YOLOv7. Additionally, the integration of voice recognition techniques enhances the algorithm's capabilities for video-based object detection, as demonstrated in YOLOv7. The proposed algorithm undergoes rigorous evaluation against state-of-the-art benchmarks, showcasing superior performance in terms of both detection accuracy and computational efficiency. Experimental results on various datasets confirm the effectiveness of YOLOv8 across diverse scenarios, further validating its suitability for real-world applications. This paper contributes to the ongoing advancements in object detection research by presenting YOLOv8 as a versatile and high-performing algorithm, poised to address the evolving needs of computer vision systems. © 2024 IEEE.

关键词： Object detection

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Artificial Intelligence-based Solution for the Prediction of Power Consumption in Electronics and Software applications

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IETE Journal of Research 2024年第1期70卷 356-371页

作者： C, Savitha Khampariya, Prabodh Singh, Kamred Udham Kumar, Ankit Singh, Teekam Swarup, Chetan Department of Electronics and Communication Engineering Sri Satya Sai University of Technology & Medical Sciences Sehore India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Computer Engineering and Applications GLA University Mathura281 406 India School of Computer Science University of Petroleum and Energy Studies Dehradun India Basic Science College of Science and Theoretical Studies Saudi Electronic University Riyadh-Male Campus Riyadh11673 Saudi Arabia

As new computing paradigms such as mobile grids and clouds become more commonplace, mobile devices are becoming increasingly attractive to scientists and HPC users who need high-performance computing capabilities. There are still many challenges to design software that uses mobile devices, such as their restricted capabilities compared to traditional devices such as PCs and servers. It’s even more critical to remember that batteries are the primary power source for mobile devices. Thus people delete programmes that severely reduce their battery life. The upshot is that it is common for developers to have no idea how much power specific hardware and software components use. So even if they have been trained in software development, they still require specific standards and skills to design energy-efficient apps. This Paper investigates ways to minimize mobile device energy usage by restructuring source code. However, we did not overlook the prospect of applying our findings to other domains, such as recurring demanding computational cores in scientific applications. Specifically, this research contributes to the field of Green Computing and mobile software development. © 2024 IETE.

关键词： Energy efficiency

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Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

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