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International Symposium on Digital Forensic and Security (ISDFS)

作者： Abhay Kamath Aditya Joshi Aditya Sharma Nikhil R Shetty Lenish Pramiee Dept. of Computer Science and Engineering PES University Bangalore India

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The dark web, recognized for its anonymity as well as for its encryption, poses a number of significant challenges for law enforcement and cybersecurity due to its illegal activities, which may include cybercrime and illegal trade. Classic monitoring techniques simply cannot cope with the dark web's unorganized, profusely scaled data. This paper intends to present an automated threat detection system based on transformer architecture models—BERT, DistilBERT, and DarkBERT—to maximize classification performance and confront threats in real-time. These models are fine-tuned on diverse datasets, DNRTI, Agora, and CoDA respectively, to classify and analyze threats effectively. Our approach integrates machine learning with a cloud-based infrastructure, namely Firebase, that can scale up to perform efficient classification, visualization, and threat reporting. This system proves superior to traditional deep learning models by achieving accuracy above 95 % on several threat classification tasks. In contrast to previous research works that stress the use of one-specific NLP model, our system synergically integrates multiple NLP models in layers to provide an enhanced classifier for better detection accuracy. This framework brings together automation for data ingestion, classification, and visualization for a realistic and scalable solution for cybersecurity practitioners keeping an eye on threats on the dark web.

关键词： Dark Web Accuracy Law enforcement Transformers Natural language processing Threat assessment Real-time systems Data models Stress Monitoring

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Plant Disease Detection using Image Processing and Generative Adversarial Network

Plant Disease Detection using Image Processing and Generativ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Y Teresha Nikhil John Shaji Manav Jarial Ved Vishal Mirjankar Swetha Patil Computer Science Engineering Dept. PES University Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Agriculture is a key contributor to the GDP of our country, India. Plants play a very important role in the ecosystem and plant diseases can negatively impact the agricultural industry by causing huge losses to the food supply. Plant leaves are prone to a variety of diseases, including blights, leaf spots, and other infections. Therefore, identifying diseases efficiently is crucial to pave the way for sustainable agriculture. In the field of agriculture, traditional and manual methods for detecting diseases in plants have sometimes been found to be inefficient and error prone. We therefore present a novel solution to these issues by introducing a method to automate plant disease detection in the Tomato plant leaf by using advanced Convolutional Neural Network (CNN) frameworks, image processing libraries and Generative Adversarial Networks (GANs). The pipeline of our work involves preprocessing plant image datasets, generating synthetic images using GANs, segmenting region of interests via a U-Net model, and classifying diseases with a CNN-based classifier. A deployed model ensures accurate disease identification from user input.

关键词： Plant diseases Image segmentation Plants (biology) Pipelines Ecosystems Manuals Generative adversarial networks Agriculture Libraries Convolutional neural networks

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Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learning Framework for Early Disease Prediction 5

Thyroid Health Forecaster: A Cutting-Edge Hybrid Deep Learni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Selvamuthu, C.M. Kartheek, G.L.N.V. Chandrashekhara, K.T. Thatipudi, Jayendra Gopal Vadar, Parashuram Shankar Bhattacharjee, Sandeep Tamil Nadu Chennai India Anurag Engineering college Department of Computer science and Engineering Telangana Suryapet India BMS Institute of Technology and Management Department of Information science and Engineering Bangalore India University of North Texas Dept. of Computer Science United States Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Amity University Rajarhat Newtown West Bengal Kolkata700135 India

ISBN: (纸本)9798331530013

Detecting thyroid disease early is crucial for effective management and treatment, as conditions like hypothyroidism and hyperthyroidism can significantly impact a patient's health if left untreated. Traditional diagnostic methods, primarily based on hormone level testing, often fail to detect the disease in its initial stages. This study introduces a hybrid deep learning strategy to predict thyroid disease at an earlier stage, utilizing a combination of ResNet50 for feature extraction and Support Vector Machine (SVM) for classification. The methodology involves preprocessing thyroid image data, including resizing, normalization, augmentation, noise reduction, and enhancement, followed by feature extraction and classification. The hybrid model achieved an accuracy of 95.2%, precision of 94.5%, recall of 93.8%, F1 score of 94.1%, and ROC-AUC of 97%. This approach demonstrates superior performance compared to traditional machine learning models, indicating its potential for reliable early diagnosis of thyroid conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Design and Development of a Low-Cost Communication-Based Protection System for Fishermen

Design and Development of a Low-Cost Communication-Based Pro...

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Optimization Techniques for Learning (ICOTL), International Conference on

作者： S. Sivakumar Ts. Arthi S. Premkumar B. Thillaieaswaran Dept of Computer Science and Engineering SRM Institute of Science and Technology Dept of Computer Science and Engineering Galgotia College of Engineering and Technology Greater Noida School of Computer Science and Engineering Galgotias University Greater Noida

Safety and wellbeing of fishermen in maritime environments is of utmost importance. This research seeks to evaluate the feasibility and practicability of an innovative communication-based system designed specifically to protect fisherman. Its main goal is providing an easy, cost-effective solution that improves both safety and communication abilities of these professionals. The system proposed here aims to promote fishers' welfare and safety at its core. It seeks to reduce risks when fishing by employing an infrared-sound wave communication infrastructure that reduces risks while simultaneously increasing efficiency compared to standalone systems. This research investigates scenarios where this combination proves superior. This communication-based system’s comprehensive sensor network features both aerial and terrestrial elements. The system seamlessly transfers sensor information, so fishermen have access to vital information that will enhance their situational awareness and facilitate sound decision-making processes. This system employs small sound waves that travel along the surface of the ocean to transmit digital information in binary format as 1s and 0s. They are created by an underwater transmitter emitting ultrasonic signal, effectively creating a communication link between underwater elements and surface ones. This research seeks to address a critical gap in maritime security by proposing and testing a communication-based protection system specifically tailored to fishermen. This system uses infrared, sound wave and other technologies to facilitate data exchange among various sensors; ultimately resulting in a robust tool with low costs for improving communication and security capabilities for fishermen.

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A Conceptual Hand Gesture Controlled Web Game Using Webcam

A Conceptual Hand Gesture Controlled Web Game Using Webcam

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Sourav Garodia Sajjadul Islam Somon Lamia Rukhsara Israt Jahan Tapasy Rabeya Dept of Computer Science and Engineering Daffodil International University Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

This paper outlines the development of a conceptual hand gesture-controlled web game using a webcam, leveraging advanced computer vision techniques. Our conceptual system employs the YOLOv8 algorithm for real-time hand gesture recognition, transforming webcam-captured images into game commands with high accuracy and low latency. The study highlights the creation and annotation of a custom dataset containing 389 images, categorized into four distinct hand gestures. Extensive preprocessing and data augmentation were performed to enhance model performance. The YOLOv8 model achieved a mean average precision (mAP) score of 98.8%, outperforming existing methods. Our findings demonstrate the potential for webcam-based gesture recognition systems to provide intuitive and immersive gaming experiences, providing an affordable substitute for specialized hardware. Future work will concentrate on extending the dataset and exploring advanced deep learning architectures to further improve the system's adaptability and accuracy.

关键词： Hands Human computer interaction Accuracy Webcams Deep architecture Games Gesture recognition Data augmentation Real-time systems Fifth Industrial Revolution

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Classification and Structuring of DL-Based Method for Finding Alzheimer’s Disease through MRI Images

Classification and Structuring of DL-Based Method for Findin...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Tripti Tripathi Rakesh Kumar Dept. of computer science and engineering MMMUT Gorakhpur India

Alzheimer’s disease (AD) is a type of neurological disorder that progresses over a period and affects a significant number of people worldwide. Early detection of AD is necessary for rapid intervention and improved outcomes for patients. In this paper, we compare the performance of four widely used Deep Convolutional Neural Network (DCNN) architectures: CNN, VGG16, InceptionV3, and DenseNet121. The study focuses on the use of DCNNs for AD identification. Our dataset is collected from the Kaggle website and preprocessed such that the inputs are identical for every model. Several criteria are used to assess the DCNN architecture’s performance, including recall, accuracy, precision, and f1-score. By looking at individual architecture’s predictive capabilities, we may discover further the benefits and drawbacks of AD detection jobs. Also, transfer learning techniques employ pre-trained weights from large neuroimaging datasets to enhance model performance and generalization abilities. This study contributes to the increasing body of knowledge in AD research by identifying the ideal DCNN architecture for fast and accurate AD diagnosis.

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A Multi-modal Approach of Sentiment Analysis on Samsung Cell Phone Review

A Multi-modal Approach of Sentiment Analysis on Samsung Cell...

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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Muhammd Nazim Uddin Md.Anisul Islam Dept of Computer Science and Engineering International Islamic University Chittagong

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Sentiment analysis, the scientific art of understanding human emotions. The motive of sentiment analysis is to extract and predict human emotions and sentiment through text, speech or facial expressions. It is proposed an integrated study that uses text review given by buyers of Samsung phones at Amazon to analyze sentiments and satisfaction towards a Samsung phone. In research, it has been seen that potential customers look for text reviews besides numeric rating to get an overview of positive and negative impressions of the product. This research helps to get critical insights for analyzing customers’ text review and giving a sentiment score for a specific text review by sentiment analysis algorithmic model. This research compares numeric and text reviews based on sentiment scores and predicts overall customer sentiment based on a text review’s sentiment score. The objective of this research is to analyze customer’s sentiment on Samsung cell phone using a multimodal approach (TextBlob, VADER, and LSTM). It extracted positive and negative features of Samsung brand or product as per buyers’ opinion, which is called opinion mining using Word Cloud dictionary library of NLP. Applied LDA model for topic modeling, used XGBoost to extract feature importance for sentiment classification, implemented VADER and TextBlob algorithm for sentiment analysis, and tested accuracy to VADER and TextBlob, applying LSTM for sentiment *** research’s implication is that both customers and brand understand the recent market trends directly through the customer’s sentiment. Customers can decide to buy or not a product; the brand can find out where to improve a product.

关键词： Sentiment analysis Analytical models Technological innovation Reviews Cellular phones Predictive models Feature extraction Prediction algorithms Tag clouds Long short term memory

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Smart Land Use Planning: Integrating Geographic Information Systems (GIS) with Graph Neural Networks (GNN) for Hazard Identification

Smart Land Use Planning: Integrating Geographic Information ...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： S Balaji Ranjani G. CVSN Reddy School of Computer Science Engineering RVU Dept of ETE RVCE

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

This paper presents a novel approach to smart land use planning by integrating Geographic Information Systems (GIS) with Graph Neural Networks (GNN). The primary objectives are to conduct satellite imagery analysis for land use patterns, determine suitable zones for commercial and residential development, analyze vulnerability to natural hazards, develop a web-based platform for land use planning, and evaluate the methodology's performance on Indian cities. By leveraging the spatial analysis capabilities of GIS and the predictive power of GNN, we aim to enhance the efficiency and accuracy of urban planning processes.

关键词： Land use planning Urban planning Land surface Reinforcement learning Programming Graph neural networks Hazards Satellite images Planning Geographic information systems

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Early Detection and Classification of Breast Cancer Using Deep Learning Techniques

arXiv

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arXiv 2025年

作者： Labonno, Mst. Mumtahina Asadujjaman, D.M. Rahman, Md. Mahfujur Tamim, Abdullah Ferdous, Mst. Jannatul Mahi, Rafi Muttaki Dept. of Computer Science and Engineering Varendra University Rajshahi Bangladesh

Breast cancer is one of the deadliest cancers causing about massive number of patients to die annually all over the world according to the WHO. It is a kind of cancer that develops when the tissues of the breast grow rapidly and unboundly. This fatality rate can be prevented if the cancer is detected before it gets malignant. Using automation for early-age detection of breast cancer, Artificial Intelligence and Machine Learning technologies can be implemented for the best outcome. In this study, we are using the Breast Cancer Image Classification dataset collected from the Kaggle depository, which comprises 9248 Breast Ultrasound Images and is classified into three categories: Benign, Malignant, and Normal which refers to noncancerous, cancerous, and normal *** research introduces three pretrained model featuring custom classifiers that includes ResNet50, MobileNet, and VGG16, along with a custom CNN model utilizing the ReLU activation *** models ResNet50, MobileNet, VGG16, and a custom CNN recorded accuracies of 98.41%, 97.91%, 98.19%, and 92.94% on the dataset, correspondingly, with ResNet50 achieving the highest accuracy of 98.41%.This model, with its deep and powerful architecture, is particularly successful in detecting aberrant cells as well as cancerous or non-cancerous tumors. These accuracies show that the Machine Learning methods are more compatible for the classification and early detection of breast cancer. © 2025, CC BY.

关键词： Lung cancer

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Optimising Vertical Mobility: Using Machine Learning to Reduce Passenger Wait Time in Elevators

Optimising Vertical Mobility: Using Machine Learning to Redu...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Navjot Singh Talwandi Navpreet Kaur Walia Dept of Computer Science Engineering Chandigarh University Punjab India

Elevators are essential in today’s urban infrastructure but struggle to keep pace with increasing demand, resulting in long passenger wait times This research aims to transform vertical transportation through better machine learning each on to increase the efficiency of the elevator proposes an innovative way to streamline elevator operation, ultimately reducing passenger waiting time and improving overall user satisfaction.

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