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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mishra, Mihir Mishra, Reeta Pandey, Anjali Mintu Ishaan Gopal Department of Computer Science & Engineering Delhi Technical Campus UP Greater Noida India Iilm University School of Computer Science & Engineering UP Greater Noida India Manav Rachna International Institute of Research & studies School of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350350067

Detecting emotions from speech is an excitingarea of research that can improve how we interact with computers and understand mental health. In our study, we explore how to build a system that can understand different emotions using speech and psychological information. We gather data from various sources like TESS, Crema-D, RAVDESS, SAVEE, and our own custom dataset to create a diverse collection of emotions. To build our system, we use different types of neural networks like CNNs, RNNs, and transformers. After testing our system extensively, we find that it can accurately detect emotions about 77% of the time, which is pretty good. We also look into how our system learns about emotions, which can help us better understand how humans process emotions. Our work not only improves emotion detection technology but also has important implications for making computers more empathetic in how they interact with people. © 2024 IEEE.

关键词： Emotion Recognition

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Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sharma, Divya Sharma, Shilpa Patil, Harshal Bhatnagar, Vaibhav Manipal University Jaipur Department of Computer Applications Jaipur India Symbiosis Institute of Technology Symbiosis International Computer Science and Engineering Pune India

ISBN: (纸本)9798350335774

Image dehazing and image defogging work towards the same goal, which is to reconstruct hazy or foggy images by eliminating noise. Among the many dehazing and defogging algorithms available, Dark Channel Prior (DCP) is among the most effective algorithm. DCP employs the dark pixels from the noisy image to perform image reconstruction using the standard four-step process. In this paper, we implement DCP single image dehazing for license plate images. Specifically, the transmission map refinement was implemented using the guided filter. The implemented model performs the DCP implementation along with its five important parameter evaluations and analyzes the performance. The paper concludes with a comparative analysis for five parameters that are patch size, epsilon value, window radius, value of and finally pixel values. © 2023 IEEE.

关键词： Image reconstruction

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Securing the Cyber Supply Chain: A Risk-based Approach to Threat Assessment and Mitigation 4

Securing the Cyber Supply Chain: A Risk-based Approach to Th...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Phani Prathyusha, J.R.V.S.L.P. Esther Jyothi, V. Jhansi, Vadlamudi Sampreeth Chowdary, N. Madhuri, A. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350300093

The system of Cyber Supply Chain (CSC) is characterized by its complexity, consisting of several subsystems, each responsible for a distinct set of responsibilities. Securing the supply chain presents a challenge due to the presence of vulnerabilities and threats throughout the system that has the potential to be taken advantage of at any time, considering that any component of the system is susceptible to such attacks. As a result, supply chain security is difficult to achieve. This has the potential to create a significant interruption to the overall continuity of the company. Therefore, it is of the utmost importance to identify the hazards and make educated guesses about their likely outcomes so that organizations can take the appropriate precautions to ensure the safety of their supply chains. By leveraging a range of factors, such as the expertise and incentives of threat actors, Tactics, Techniques, and Procedures (TT and P), as well as Indicators of Compromise (IoC), the analysis of Cyber Threat Intelligence (CTI) offers valuable information on both identified ansignd unidentified cybersecurity threats. In order to increase the safety of the cyber supply chain, the purpose of this article is to investigate and speculate on potential dangers. The CTI and Machine Learning (ML) approaches have been employed by us in order to study and forecast the risks based on the CTI attributes. This makes it possible to detect the inherent CSC vulnerabilities, which enables suitable control. To enhance the overall security of computer systems, it is imperative to implement specific actions, including the collection of CTI data and the adoption of various machine learning techniques. These techniques encompass Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Cat Boost, and Gradient Boost, which are employed in analyzing the Microsoft Malware Prediction dataset to create predictive analytics. This is done in order to illustrate t

关键词： Supply chains

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A Pattern Recognition Model: Hand Gestures Recognition using Convolutional Neural Networks

A Pattern Recognition Model: Hand Gestures Recognition using...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Ayyappa Reddy, D. Esther Jyothi, V. Kasi Viswanath, J. Sampreeth Chowdary, N. Swapna, D. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350333602

Human Beings have the natural ability to recognize body and sign language easily. This is possible because of vision and synaptic interactions formed during brain development. Humans also can pick up contextual information to understand body and sign language. In order to replicate this ability in computers, it is highly required to solve several issues-the ability to differentiate between objects of interest and the background. Choosing image capture technology and image classification algorithms etc. So, Hand gesture recognition is quickly becoming an important and relevant technology. Given the recent growth of AR and VR technologies, Hand gesture recognition has become an exciting new technology field. But currently, hand gestures are very specialized and haven't acquired mainstream adoption. This study intends to show that gesture recognition can enhance many aspects of computer work and teaching. In our predicted model, the system works by the user starting the program, which would start providing the live webcam feed. The user then makes the hand gesture inside the frame of detection shown on the screen. When the hand gesture is made, the gesture is segmented and isolated. Some possible applications are to reduce the use of a physical keyboard and mouse by using hand gestures recognition to control computer operations like Pause/Play, close and open windows, and manipulate media controls. Another application is sign language. Hand gesture recognition can be used for communication and teaching purposes. © 2023 IEEE.

关键词： Gesture recognition

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Robust Sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts 5

Robust Sensor Networks: Applying AI for Fault Identification...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Ramakrishna, M. Prabhu, Matti Nidhi Dept. of Computer and Communication Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Dept. of Computer Science and Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal576104 India

ISBN: (纸本)9798331518097

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s. © 2024 IEEE.

关键词： Random forests

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Designing an Intelligent Model that Employs Advanced Machine Learning Techniques to Optimize Disease Prediction and Classification in Humans 1

Designing an Intelligent Model that Employs Advanced Machine...

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

作者： Kumari, Sudesh Lekshmi, V. Arathi Chandran, R.I. Shankar, Jaya Vishnu Priya, P.M. Bps Women University Department of Electronics and Communication Engineering Sonipat India Christ Nagar College Department of Computer Science and Applications Kerala Maranalloor India Sree Narayana Institute of Technology Department of Computer Science and Engineering Kerala Adoor India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350307795

Machine learning (ML) has emerged as a powerful tool with applications across diverse fields, including clinical diagnostics. With the growing prevalence of heart and liver diseases worldwide, early detection is paramount for saving lives. This study presents a hybrid ML model that combines the support vector machine (SVM) approach with a modified particle swarm optimization (PSO) model for the classification of heart and liver disease data. Utilizing datasets from the UCI ML repository, we evaluate the model's performance using various metrics, including categorization accuracy, error rates, correctness, recall, and the F1 score. Comparative analyses with traditional SVM and other hybrid algorithms such as the PSOSVM and Crazy PSO-SVM (CPSOSVM) are conducted. The experiments encompass the prediction of cardiac and liver illness. Results demonstrate that the designed Cauchy Crazy PSOSVM (CCPSOSVM) algorithm outperforms other approaches, achieving the greatest accuracy rate and least error rate. While this study focuses on cardiac and hepatic illness prediction, the CCPSOSVM algorithm holds promise for the extension of disease prediction across various medical domains. © 2024 IEEE.

关键词： Support vector machines

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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computer Systems science & engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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Deep Learning-Based Approach for Detecting Copy-Move Forgery in Digital Images 27

Deep Learning-Based Approach for Detecting Copy-Move Forgery...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Dar, Navid Ahmad Atsya, Rani Kumar, Anuj Sharda University dept. of Computer Science and Engineering UP Greater Noida India Greater Noida Institute of Technology dept. of computer applications UP Greater Noida India

ISBN: (纸本)9798350392319

Copying and pasting a portion of the same image to hide another portion is the most common method of fraudulent image enhancement. Cloning a restricted portion of an image and pasting it once or more inside a comparable picture is known as copy-move image forgery (CMF). The procedure aims to highlight or hide a certain aspect in the finished picture, usually a person or an object. The harm brought about by the horrible deed frequently takes unexpected forms. For the automated identification of CMF, the paper proposes a Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) approach. The CASIA 2.0 dataset's, which includes both real and fake photos, was used in the study. The proposed model is simple to use, and its application shows that it is faster than previous approaches that are now regarded as state of the art. The results show that, when compared to the other currently available methods, the recommended approach achieves the highest level of accuracy (90.56%). © 2024 IEEE.

关键词： Generative adversarial networks

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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