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Penguin Search Optimization with Deep learning Based Cybersecurity Malware Spectrogram image Classification 1st

Penguin Search Optimization with Deep Learning Based Cyberse...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Jeyalakshmi, J. Santhiya, M. Jegatha, R. Department of CSE Amrita Vishwa Vidhyapeetham Coimbatore India Department of CSE Rajalakshmi Engineering College Mevalurkuppam India Department of IT Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9783031730641

Background: Cybercriminals typically employ malware to achieve their objectives, which include botnets, ransomware, etc. Encryption, packaging, and polymorphism make it difficult to identify malware files, especially when they are generated in large quantities every day. The new technique of image-based detection focuses on detecting malware by transforming it into an image and classifying the image as benign or malicious. The raw binary of malware is converted into a grayscale image, and neural networks are used to classify it as benign or malicious, or to classify the image to its respective malware family. Proposed Method: This system proposal introduces a new Penguin Search Optimisation with Deep learning-based image Classification (PSODL-IC) for Malware Spectrogram. The presented PSODL-IC technique uses spectrogram images in conjunction with the DL model to classify malware files and differentiate them from benign files. In the presented PSODL-IC technique, the noise elimination stage employs Gaussian filtration (GF). In addition, the presented PSODL-IC method employs the Xception feature extractor in conjunction with a PSO-based hyperparameter optimizer. In this investigation, the auto encoder (AE) is utilised for classification purposes. Outcome and Discussion: Extensive experimental analysis has demonstrated that the PSODL-IC technique yields superior results to other deep learning models, with 97.21 percent accuracy. The obtained results demonstrate the advantages of the PSODL-IC method over other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spectrographs

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A Multi Model Deep learning Framework for Sarcasm Detection in Regional Social Media: An Overview

A Multi Model Deep Learning Framework for Sarcasm Detection ...

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Anilkumar M. Bhadgale M. Maheswari Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These architectures, combined with Natural Language processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual

关键词： Deep learning Surveys Social networking (online) Focusing Feature extraction Transformers Natural language processing Complexity theory Cultural differences Context modeling

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A machine learning Approaches with Special Emphasis for Identification of Plant Leaf Diseases in Crop Yield Prediction 1st

A Machine Learning Approaches with Special Emphasis for Iden...

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Babu, Ch Ravindra Jyothi, E.V.N. Prassana, K. Naga Jayanth, T. Siva Sandani, Sk. Chand, S. Veera Venkata Gopi Shwetha, R.J. Pace Institute of Technology and Sciences AP Ongole India IV B. Tech CSIT Pace Institute of Technology and Sciences AP Ongole India Global Payex Technologies Pvt. Ltd. Mumbai India

ISBN: (纸本)9783031756047

Productivity in farming is very essential to the Indian economic system. When a plant gets any disease it can significantly reduce production, cost money and reduce the quality and quantity of farm produce. To prevent agriculture from losing quantity and quality, it is essential to recognise diseases of plants. Plant disease identification is receiving more and more attention these days since large crops area is being monitored. Monitoring plant health and identifying diseases is critical for sustainable agriculture. This requires a lot of work, knowledge of plant diseases, and a lot of processing time. Utilizing machine learning algorithms, To predict increased agricultural yields, crop yield projections are made. One of the difficult problems in the agricultural industry is this. Given the growing significance of agricultural yield prediction, with a concentrate on agricultural yield prediction, it is important to apply machine learning techniques to crop yield predictions. Techniques like decision tree, support vector machine, Bayesian Network, and random forest, among others, are used in machine learning support in the automatic identification of plant disease based on the plant’s visual symptoms. This research presents an analysis of several machine learning approaches for plant disease prediction. The outcomes demonstrate that this technique conducted illness diagnosis with success and enhanced diffusion pattern analysis and disease progression level detection accuracy. Thus machine learning approaches with special emphasis for identification of plant leaf diseases in crop yield prediction results show that plant diseases can be accurately classified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Large Language Models Based Framework for Finding Cloth Quality 15th

Large Language Models Based Framework for Finding Cloth Qual...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Devi Priya, R. Tharunika, L. Shreya, S. Rajasekaran, T. Sivasankari, M. Shanmuga Priya, T. Sivaraj, R. KPR Institute of Engineering and Technology Coimbatore India Nandha Engineering College Erode India

ISBN: (纸本)9783031810824

The rapid growth of e-commerce and the increasing demand for online clothing shopping have elevated the importance of accurate clothing quality assessment. In response to this demand, we present a novel mobile application that leverages deep learning techniques for pixel-level analysis on clothing items. The proposed application empowers users to capture images of clothing and receive real-time quality assessments in large datasets. The inputs will be the collection of large set of images for training and traditional machine learning models cannot provide sufficient accuracy. This paper addresses the challenge of quality assessment through a comprehensive pipeline Large Language models (LLM) based deep learning algorithm called ClothQualityFinder (CQF) that involves data collection, preprocessing and the application of deep learning algorithms namely Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). This paper includes a detailed examination of pixel-level analysis, encompassing aspects such as color analysis, texture recognition, and contour detection. The resulting model demonstrates good accuracy, making it a valuable tool for consumers and retailers alike. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloth Quality Finder Convolutional Neural Networks image processing Large Language models Pixel Level Analysis

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Transfer-learning Method for Automatic Breast Cancer Identification and Classification 15th

Transfer-Learning Method for Automatic Breast Cancer Identif...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Rani, B. Kavitha Unissa, Raheem Kumar, M. Naveen Sarika, T. Sailaja, Y. Upender, T. CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031810824

Another of the deadliest illnesses is cancer. Furthermore, the least frequent cancer in women is breast cancer, which can be lethal if left untreated. Even with significant advancements in medical technology over the years, the only reliable method for detecting breast cancer is still a biopsy. Microscopic histology photos can be used by pathologists to identify malignancy. It’s difficult to see cancer;it takes a lot of talent, imagination, and concentration. Therefore, a more rapid and precise method of identifying breast cancer is required. An effective partially or fully computer-controlled diagnosis system is being researched in several ways due to advances in machine learning and image processing. Cancer of the invasive ductal was identified and categorized in this study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： convolutional neural network (CNN) dataset of breast cancer images Deep learning Inception Resnet VGG19

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ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation 1st

ECLNet: Efficient Convolution with Lite Transformer for 3D M...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Zheng, Lei Zhang, Yuzhong Chen, Huagui Guo, Yuheng Zhou, Lei School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Attrsense Shangha Shanghai China Shanghai Waigaoqiao Shipbuilding and Offshore University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9789819633487

Convolutional neural networks (CNNs) have been significantly advanced in the field of medical imaging in recent years. The implementation of U-shaped architectures has proven particularly effective, demonstrating exceptional results across various image segmentation challenges. Despite these achievements, convolutional operations face challenges in capturing broad-scale global context and long-distance semantic relationships. Therefore, the ECLNet is presented in this paper, a converter with an architecture similar to U-Net that was specifically designed for medical image segmentation. With the help of the Transformer architecture, complete global context is extracted by this model through the processing of feature maps produced by the CNN as input sequences. To achieve accurate feature localization, the CNN feature map is combined with the global context information. It is claimed that the Transformer design can be utilized as a reliable encoder for medical image segmentation applications. Its combination with convolutional networks enhances segmentation efficiency by facilitating the restoration of local spatial information. Finally, extensive experiments on one public and one private datasets show that our proposed algorithm performs better than several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Comprehensive Review of Cybersecurity Vulnerabilities and Safety Concerns in Autonomous Vehicles

Comprehensive Review of Cybersecurity Vulnerabilities and Sa...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE international conference on

作者： Uppalapati Sudheer Kumar Sukanta Halder Basant Kumar Sethi Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat India Department of Electrical Engineering Indian Institute of Technology(ISM) dhanbad Dhanbad India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weight sensors, speed sensors, tire pressure sensors, 3D imaging cameras, satellite-based positioning systems, and advanced radars like light detection and ranging (LiDAR) and 3D radar. AVs utilize these components, along with highly complex control algorithms as their central processing units, to perform autonomous driving, making them vulnerable to cybersecurity attacks. These security attacks raise significant safety concerns for AVs. This article provides a comprehensive review of various cybersecurity threats associated with AVs and presents potential solutions. Additionally, it examines the vulnerabilities of the sophisticated equipment used in AVs. This article aims to enhance the understanding of the safety and security concerns associated with AVs and offers insights into the importance of cybersecurity in this evolving technological field. Furthermore, this article addresses the current state of cybersecurity measures in AVs, evaluating the effectiveness of existing protocols and identifying areas that require improvement. It also explores emerging technologies and strategies that can enhance the security of AVs, such as blockchain for secure data transactions, machine learning for threat detection, and robust encryption methods.

关键词： image sensors Radar imaging Threat assessment Sensor systems Safety Sensors Encryption Security Computer security Autonomous vehicles

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Indoor Visible Light Positioning System Based on the image Sensor and CNN-GRU Fusion Neural Network 1st

Indoor Visible Light Positioning System Based on the Image S...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Liu, Guoqing Qin, Ling Du, Yongxing Zhao, Desheng School of Digital and Intelligence Industry Inner Mongolia University of Science and Technology Baotou China

ISBN: (纸本)9789819633487

With the continuous development of artificial information technology, visible light positioning (VLP) based on deep learning has emerged as a hotspot for research on indoor localization technology. To improve the accuracy of indoor positioning, this paper proposes a convolutional neural network-gated recurrent unit (CNN-GRU) fusion neural network-based indoor VLP system. The system uses a single circular LED to transmit ID data and a common smartphone to capture feature images. To obtain light-emitting diode (LED) position information, this system adopts a method of extracting pixel values of the LED projection ellipse’s major axis for demodulation. In the coordinate prediction process, the feature parameters such as the location of the centre of mass, the length of the long and short axes, and the area of the ellipse in the image projection are input into the network for training, and the trained model is used to predict the coordinates of the test set. The results of this experiment show that the average positioning error is 3.79 cm in the 2.25 m × 2.25 m × 2.25 m experimental area, which can meet the positioning requirements of the indoor environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Navigating the Edge: A Comprehensive Comparison of machine learning Algorithms on Prominent Edge Platforms 4th

Navigating the Edge: A Comprehensive Comparison of Machine...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Sayed, Aya Nabil Al-Hasan, Tamim Mahmud Bensaali, Faycal Nhlabatsi, Armstrong Department of Electrical Engineering College of Engineering Qatar University Doha Qatar KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

ISBN: (纸本)9783031821493

As the demand for deploying machine learning (ML) and Deep learning (DL) models at the network’s edge continues to grow, it becomes imperative to evaluate and compare the performance of various edge platforms. This study introduces an innovative edge-based pipeline for analyzing home energy and environmental data and addressing challenges in managing imbalanced label distributions. The comparison between random under-sampling and over-sampling strategies reveals the superiority of over-sampling, significantly improving accuracy and f1-score. More specifically, over-sampling using the Decision Tree (DT) technique enhanced accuracy and f1-score by 4.97% and 9.24%, respectively, when compared to the original results. The paper expands its contribution by conducting a thorough performance evaluation of various edge platforms, emphasizing critical metrics. The evaluated edge platforms include a Raspberry Pi 4b+, Jetson Nano Developer Kit, Odroid XU4, and Coral Dev Board. The study conducted an exhaustive comparison of the runtime of these prominent edge platforms when running ML/DL algorithms for tabular and image datasets during the testing phase. The Jetson Nano, particularly with its built-in GPU, demonstrates superior performance in ML/DL tasks, showcasing accelerated processing times for Convolutional Neural Network (CNN)-based algorithms. These findings emphasize the efficacy of the Jetson Nano in handling computationally intensive tasks for edge-based applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Identifying Paddy Crop Disease Using Enhanced Deep learning Technique 1st

Identifying Paddy Crop Disease Using Enhanced Deep Learni...

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1st international Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Subramanian, Ganapathy Neduncheliyan Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9783031823886

The consequences of extreme weather changes on crops, the growing global population, and numerous illnesses that emerge at a severe level all have an impact on agricultural production and food security. Farmers employ expensive disease management strategies that sometimes outweigh output losses. Because it would be too expensive for them to install crop protection measures at the early beginning of the illnesses, which results in very little advantages, the farmers would only act when it was too late to stop the disease from spreading. It was suggested that computer orders using technology for calculating vision may be beneficial in a range of original-globe situations. It may be used in crop security strategies since it employs artificial intelligence, graphics, and image processing to detect illnesses, pests, and malnutrition. machine learning (ML) approaches are being utilised in the ongoing study of disease detection from plant photographs. For recognising paddy leaf disease, a unique deep neural network (DNN) classification model is employed using plant picture data. Existing system like CNN, TL and ANN gives the mere accuracy results. There is drawback in identifying the accurate crop diseases. The proposed hybrid ECNN and CSA algorithm give more accuracy when compared to the existing system. It increases the accuracy level by 95%. The hybrid ECNN & CSA based designs shown amazing success in crop disease prediction and categorization using images. These models require a lot of training data, though, and they are computationally costly. The proposed enhanced hybrid deep learning based hybrid algorithm model identifying the diseases in the paddy leaves in a most significant manner. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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