https://journal.hmjournals.com/index.php/JIPIRS/issue/feedJournal of Image Processing and Intelligent Remote Sensing(JIPIRS) ISSN 2815-09532024-03-07T09:54:12+00:00Editor in Chiefeditor.jipirs@gmail.comOpen Journal Systems<p>The<strong> Journal of Image Processing and Intelligent Remote Sensing (JIPIRS) havining</strong> <strong>ISSN 2815-0953</strong> is a Double Blind, Peer-reviewed, open access journal that provides publication of articles in all areas of Image Processing, Pattern Recognition, Remore Sensing and related disciplines. The objective of this journal is to provide a veritable platform for scientists and researchers all over the world to promote, share, and discuss a variety of innovative ideas and developments in all areas of Image Processing,Pattern Recognition, and Remore Sensing<strong>.</strong></p>https://journal.hmjournals.com/index.php/JIPIRS/article/view/3562Exploring the Effectiveness of Machine and Deep Learning Techniques for Android Malware Detection2024-01-24T10:07:12+00:00Khalid Murad AbdullahKhalid.mu.abdullah@gmail.comAhmed Adnan Hadiah2036@gmail.com<p>The increasing occurrence of Android devices, coupled with their get entry to to touchy and personal information, has made them a high goal for malware developers. The open-supply nature of the Android platform has contributed to the developing vulnerability of malware assaults. presently, Android malware (AM) analysis strategies may be labeled into foremost categories: static evaluation and dynamic evaluation. These techniques are employed to analyze and understand the behavior of AM to mitigate its impact. This research explores the performance of DL model architectures, such as CNN-GRU, as well as traditional ML algorithms including SVM, Random Forest (RF), and decision tree (DT). The DT model achieves the highest accuracy (ACC) of 0.93, followed by RF (0.89), CNN-GRU (0.91), and SVM (0.90). These findings contribute valuable insights for the development of effective malware detection systems, emphasizing the suitability and effectiveness of the examined models in identifying AM.</p>2024-02-01T00:00:00+00:00Copyright (c) 2024 Authorshttps://journal.hmjournals.com/index.php/JIPIRS/article/view/3678Deep Learning Strategies for 5G and LTE Spectrum Sensing Communication2024-02-17T09:18:18+00:00Suham A. AlbderiKin.shm@atu.edu.iq<p>: The idea of 5G innovations is a prevalent instrument for the pace of transmission and gathering of data and the accessibility of permitting all over the place. Notwithstanding that the fifth era convergences will embrace a keen procedure for the data transmission process. Sending and getting signals work in high coordination in 5G networks, since this innovation arranges flexible, geostationary earthbound correspondence with other medium and little circuit correspondences with short steering in straight correspondences, and the correspondence incorporates signal processing as well as way finding. In this study the responsiveness improvement of the correspondence range will be tested by applying blended deep learning methods, in which the data cross-over will be diminished with the upgraded smart control. Utilizing blended deep learning methods, this study exhibits the huge difficulties presented by 5G transmissions in keenly detecting the LTE signal range and different data in 5G remote sensor networks. Way obstructions are recognized as the essential hindrance. The states of the correspondence framework ought to be considered while plotting the network and sensors for the fifth era.</p>2024-02-17T00:00:00+00:00Copyright (c) 2024 Authorshttps://journal.hmjournals.com/index.php/JIPIRS/article/view/3710Diabetic Retinopathy Detection Using InceptionResnet-V2 and Densenet1212024-02-24T08:25:18+00:00Gangumolu Harsha Vardhanharshavardhan5905@gmail.comMeda Venkata Sai Jyoshnaharshavardhan5905@gmail.comPamarthi Kasi Viswanathpamarthiviswanath17@gmail.comShaik ZubayrShaikzubayr20@gmail.comVelaga Sravanthvelagasravanth460@gmail.com<p>This project addresses the global health challenge posed by the prevalence of diabetic retinopathy (DR) by developing an efficient automated diagnostic system. The dataset, consisting of diverse high-resolution retinal images, underwent preprocessing to categorize images into No DR (0) and DR (1-4) classes. The First initial binary classification model using a Convolutional Neural Network (CNN) discriminated between healthy and diseased retinas. Subsequently, The second multi-class CNN model was designed to predict the severity of diabetic retinopathy (DR) across a spectrum from mild (1) to proliferative DR (4), enabling a fine-grained analysis for early identification of cases requiring urgent intervention. To address real-world complexities, potential noise in the dataset, including artifacts and exposure variations, was acknowledged. The CNN models were designed to exhibit resilience to these challenges, ensuring robust performance in clinical settings. Preprocessing is considered the common occurrence of image inversion in retinal imaging by incorporating anatomical features, such as macula position and notches, to correctly identify image orientation and enhance result interpretability. The proposed automated analysis system demonstrated promising results in accurately categorizing retinal images into No DR and DR, as well as assigning severity scores for diabetic retinopathy. This project contributes significantly to computer-aided diagnostics, Supplying a dependable instrument for promptly identifying and addressing cases of diabetic retinopathy.</p>2024-02-24T00:00:00+00:00Copyright (c) 2024 Authorshttps://journal.hmjournals.com/index.php/JIPIRS/article/view/3805Recognition of Angry and Happy Facial Expressions with Local Binary Pattern and Support Vector Machine2024-03-07T09:06:14+00:00Ilham Muhammad Furqonilhammfurqon@gmail.comEnny Itje Selailhammfurqon@gmail.com<p>Looks are a huge sort of nonverbal correspondence for getting a handle on a singular's opinions and sentiments. Look affirmation has various potential applications, including security, tutoring, and prosperity systems. The justification for this assessment is for a look affirmation system using area twofold model and sponsorship vector machine techniques. It is typical that this structure can perceive two kinds of sentiments, to be explicit angry and delighted to get the right assumption. First accumulate fundamental data and assistant data from Kaggle and phone cameras, second preprocessing data with grayscale and resizing, third surface features using Area equal model extraction, fourth assist vector with machining about look request used, finally structure evaluation considering precision, exactness, survey, and F-1 worth. The results show that the look affirmation system works honorably on area matched model and sponsorship vector machine. On this investigation dataset, support vector machine gathering on direct part has favored execution over winding reason ability segment. The most significant precision was achieved with the straight part at 86.25%. This study assumes that the look affirmation structure can work outstandingly with neighborhood matched model and sponsorship vector machine. The straight part shows better execution on this investigation dataset. This assessment can be also advanced by adding more looks, incorporate extraction methods, and plan.</p>2024-03-07T00:00:00+00:00Copyright (c) 2024 Authorshttps://journal.hmjournals.com/index.php/JIPIRS/article/view/3808Generative AI in the Era of Transformers: Revolutionizing Natural Language Processing with LLMs2024-03-07T09:54:12+00:00Archana Balkrishna Yadavarchu.payal@gmail.com<p>The advent of Transformer models is a transformational change in the field of Natural Language Processing (NLP), where technologies are becoming rather human-like in understanding and mirroring human language. This paper highlights the impact of Generative AI, specifically the Large Language Models such as GPT, on NLP. The analysis presents the prototypical units fuelling Transformer architectures, with attention given to their applications for complex language tasks and advantages from the angle of efficiency and scalability. However, the evidence highlights substantial progress in MT, text summarization, and SA versus the baseline NLP models. This work, therefore, emphasizes the key role of using a Transformer-based LLM system as a means to grow the NLP field and can lay the foundations for developing more natural and intuitive human-computer interactions.</p>2024-03-07T00:00:00+00:00Copyright (c) 2024 Authors