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A Comparative Study in Image Fusion Using Orthogonal and Biorthogonal Wavelet 12th

A Comparative Study in Image Fusion Using Orthogonal and Bio...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Pandy, Saroj Kumar Kumar, Ankit Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain the key elements from each image. This method has been used in a number of industries, including robots, satellite images, and medicine. Researcher decision-making and the success of scientific endeavors are greatly influenced by the quality of the fused image. In this article, a comparison of the wavelets Haar and Bior 2.2 is made. In order to compare the two wavelets, different photos are employed. Based on metrics like the Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Standard Deviation (SD), the performance of the wavelets is assessed. Based on the results, it can be said that Bior 2.2 performs somewhat better than Haar in terms of picture fusion quality. We use orthogonal and biological wavelets to compare how well image fusion works. To fuse pictures, we utilize the Discrete Wavelet Transform (DWT) and the maximum selection criteria. The performance of the fused pictures is assessed using a number of factors, such as visual quality measurements. The advantages of biorthogonal wavelets over orthogonal wavelets for image fusion tasks are highlighted in this research, especially when working with numerous sensors. It emphasizes how important the decomposition level is for producing high-quality fused pictures. The results help advance image fusion methodologies and offer insightful information to academics and industry professionals involved in picture processing and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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Enhancement of Malware Detection Systems Using Mal-cGAN 8th

Enhancement of Malware Detection Systems Using Mal-cGAN

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Timmanagoudar, Harshit Preethi, P. Department of Computer Science and Engineering PES University Karnataka Bangalore560085 India

ISBN: (纸本)9789819721467

Malware detection systems that incorporate machine learning algorithms have received considerable attention because of their impressive ability to identify and combat emerging and sophisticated malware threats effectively. These systems rely on advanced techniques to analyse vast amounts of data and recognise patterns that distinguish between harmless and malicious software. The development of Generative Adversarial Networks [1] has enabled innovative techniques to enhance the capability of the malware detection systems, Mal-GAN [2]. In this research paper, an innovative approach called Mal-cGAN is introduced, aiming to enhance the performance of malware detection systems that solely work on image representations of software by synthesising image representations of malware. Drawing inspiration from the architecture of Conditional Generative Adversarial Networks (cGANs) [3], the method employs a "U-Net" [4] based generator that facilitates seamless communication between the Malware Detection System and Substitute detector with the Substitute Detector performing the role of a discriminator from typical GANs. This effective collaboration enables the generator to produce practical and highly realistic malware image representations, which in turn can bypass the detection systems and eventually used to enhance the performance of the Malware Detection System. Thorough evaluations of the model demonstrate that the Mal-cGAN generator successfully synthesizes accurate and informative image representations of malware. Notably, training the Malware Detection System on these synthesized samples leads to a substantial increase in accuracy, improving from 83.47% to 93.61%. The results depicting synthesised image representations of malware and the classification metrics highlight the effectiveness of the proposed approach and its potential to revolutionise the field of malware detection using machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pt

关键词： Generative adversarial networks

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Non-Contact Vital Prediction Using rPPG Signals 1

Non-Contact Vital Prediction Using rPPG Signals

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

作者： Madhu, Meghna Gurudas, V.R. Manjunath, Chinthakunta Naik, Praveen Kulkarni, Praveen School of Engineering and Technology Department of Computer Science and Engineering Bangalore India Dayananda Sagar University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. © 2023 IEEE.

关键词： Photoplethysmography

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Classification of Plant Disease using Machine Learning Algorithms 6

Classification of Plant Disease using Machine Learning Algor...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Appalanaidu, Majji V. Kumaravelan, G. Department of Computer Science Engineering Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science Pondicherry University Karaikal Campus Karaikal India

ISBN: (纸本)9798350374629

The identification of plant diseases has become a key topic in smart agriculture research in recent years. The productivity of agricultural products will undoubtedly increase with early plant disease identification. For the farmers, it is difficult to determine the specific diseases affecting the plant leaves. Therefore, the development of an automated system becomes an essential practice for detecting diseases. So that the remedies can be suggested according to the disease's severity. In this situation, image processing methods combined with Machine Learning (ML) algorithms have lately offered an accurate and dependable way of identifying and categorizing many plant diseases. Hence, this work investigates the importance of the deployment of various ML algorithms such as Naïve Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) in automatic classification of plant diseases on the benchmark plant village dataset. ©2024 IEEE.

关键词： Agricultural products

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Prediction of the Approval of Bank Loans Using Various Machine Learning Algorithms

Prediction of the Approval of Bank Loans Using Various Machi...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Rahman, Arifa Tur Purno, Md Rakib Hasan Mim, Samiya Akter Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350310061

Both individuals and businesses now frequently obtain loans from banks, but because banks have limited resources, they carefully consider each applicant before approving a loan. To determine who the loan can be granted to and who would be a better choice for the bank, for this reason, credit institutions go through a protracted phase of verification and identification. This study aims to employ various machine learning techniques to analyze enormous amounts of data in order to detect patterns that could be used to predict whether a loan would be approved for a particular person or organization and determine whether the borrower can afford to repay the loan or not. Another goal of this study is demonstrating how machine learning will give the banking sector a more reliable predictive modeling tool than it currently has. In this work, the data was gathered from 1000 records of applicants with 16 features from the reputed bank in the city of Dhaka, Bangladesh. Finally, the following seven data mining techniques are employed for accuracy, and suggestions are made regarding how to make the loan approval process more effective and affordable for both banks and borrowers by dramatically lowering the time and effort needed: Decision Tree, Gradient Boosting, Random Forest, Naive Bayes, Support Vector Machine, and Logistic Regression are some examples of machine learning techniques. At the conclusion of the study, the Random Forrest method has the highest accuracy. © 2023 IEEE.

关键词： Decision trees

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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CropSmart-A Crop Prediction Application Using Machine Learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security 1

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (数字)9789819752003

ISBN: (纸本)9789819751990

Image steganography is a technique for encrypting data, allowing covert communication, and enhancing data security inside image files. This study provides an in-depth analysis of image steganography, covering its core ideas, methods, uses, difficulties, and developments. Numerous embedding algorithms and methodologies, including transform domain methods and LSB-based strategies, are examined in this study. The paper highlights the need for effective defenses against steganalysis attacks, while also discussing the challenges and potential weaknesses in steganography. It examines common steganography techniques and discusses countermeasures such as adaptive embedding, key-based embedding, and algorithmic resistance. Additionally, the paper also highlights the significance of assessing the efficacy and caliber of image steganography techniques. Objective metrics such as PSNR and MSE are discussed, providing insights into the trade-offs between security and image fidelity. For those interested in understanding image steganography, its applications, and its challenges, this study offers a complete resource. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Proposing an Advanced Trending-based Grey Wolf Optimizer for Single-objective Optimization Problems 20

Proposing an Advanced Trending-based Grey Wolf Optimizer for...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Mokabberi, Amirhossein Golsorkhtabaramiri, Mehdi Varzi, Ramzan Abbasnezhad Mazandaran University of Science and Technology Department of Computer Engineering Babol Iran Islamic Azad University Babol Branch Department of Computer Engineering Babol Iran

ISBN: (纸本)9798350383942

optimization algorithms play a crucial role in solving complex problems in various domains. Single-objective optimization algorithms aim to discover the most optimal solution for a particular objective function, commonly distinguished by a single criterion or goal. Grey Wolf optimizer (GWO) is a swarm-based algorithm that has gained attention due to its simplicity and efficiency in solving optimization problems. In this article, we propose an advanced version of GWO, which is referred to as the Advanced Trending-based Grey Wolf optimizer (ATGWO), specifically tailored for single-objective optimization problems. The motivation behind this modification stems from the need to improve the performance metrics of the original GWO algorithm and avoid local optimum. By altering the algorithm's coefficients, we aim to enhance its convergence rate, exploration, and exploitation abilities. To evaluate the proposed ATGWO algorithm, we conduct simulations using 7 multimodal single-objective benchmark functions. The results suggest that although the ATGWO excels in accuracy, it has more delay in comparison with GWO. This study paves the way for future research about optimization algorithms. © 2024 IEEE.

关键词： Heuristic algorithms

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Design and Development of Tomato Leaf Classification Framework Using CNN 4

Design and Development of Tomato Leaf Classification Framewo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Lais Ezaz Ahmed, Sayed Abu Kumar, Abhishek Singh, Ranjit Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India

ISBN: (纸本)9798350387490

Crop production loss in agriculture is twenty-two percent because of biotic and abiotic stress/disease and living and non-living organisms. Producers must be able to identify these requirements from the beginning. computer vision can help discover early illness patterns and clusters. Recent Deep learning-based computer vision advancements can recognise single or multi-biotic stress in tomato plant leaves. This investigation will use the PlantVillage dataset to segment objects. The model was trained with tagged, enriched, and augmented data. The Hybrid-CNN model was used to segment sick tomato plants. In object detection, the proposed Hybrid CNN outperforms the U-Net architecture and Seg-Net when paired with instance segmentation. This study used semantic segmentation data to identify and classify single-and multiple-leaf illnesses. The Hybrid-CNN algorithm's predicted output corresponds to that of the adapted U-Net, M -SegN et, and adapted U -Net models based on state-of-art evaluation parameters with other evaluation parameters such as Intersection over Union (IoU) and Mean IoU. The proposed model outperformed competitors, processing 1004 pictures in under 30 nanoseconds. With an accuracy of 98.580/0, the suggested model outperforms modified segmentation models. © 2024 IEEE.

关键词： Plant diseases

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