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Cyber Security and Applications 2025年 3卷

作者： Panigrahi, Rasmita Padhy, Neelamadhab School of Engineering and Technology Dept. of Computer Science and Engineering GIET University Gunupur Odisha Gobriguda India

Steganography is the art and science of writing secret messages so that neither the sender nor the intended recipient knows there is a hidden message. Data hiding is the art of hiding data for various reasons, such as keeping private data, secure, confidential data, etc. With increasing data exchange over a computer network, information security has become a significant issue. There are many methods used for data hiding, and steganography is a well-known technique. Steganography is the art of invisible contact and science. Steganography is the process through which the presence of a message can be kept secret. The objective of this paper is to hide data using the LSB (Least Significant Bit) technique into images that can be detected only by the specified user. We have developed a user-friendly GUI such that it can be used with the utmost ease. This paper is motivated to hide the message stated by the user in the dialog box given within the picture. The secret text is converted to the ciphertext to make it more stable. The sender selects the cover image, and it is used to generate the secured Stegno image, which is identical to the cover image. With the support of a private or public communication network, on the other hand, the stegno image can be saved and sent to the designated user, i.e., the recipient downloads the stegno image and can retrieve the secret text concealed in the stegno image using that same application. As for the watermarking, we have visible and invisible we have used the same LSB technique. In visible watermarking, text or image is embedded in the cover image, which can be noticed easily. As for invisible watermarking, some specific text is inserted into an image, and while retrieving it, it generates a QR code^, which can be scanned to get the watermarked text. We used the three different types of cover images i.e. Gray, and RGB also estimated the performance metrics. SNR, MSE, and PSNR the three performance metrics are used, and fou

关键词： Embedding Encode, Decode Least significant bits QR Code Steganographic techniques Stego image watermarking

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Advancing Oncology Outcomes: Deploying Advanced Machine Learning Models for Early Detection and Optimized Treatment

Advancing Oncology Outcomes: Deploying Advanced Machine Lear...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khobragade, Prashant Dhankar, Praveen Kumar Motghare, Manish Golghate, Ashish Rakesh, Nitin Turkar, Hemantkumar Computer Science and Engineering G H Raisoni College of Engineering Nagpur India Pune India G H Raisoni College of Engineering and Management Dept of Artificial Intelligence Nagpur India Nagpur India S.B.Jain Institute of Technology Management and Research Department of Data Science Nagpur India

ISBN: (纸本)9798331517953

The field of oncology is changing quickly as more advanced machine learning (ML) models are added to help find cancer earlier and improve treatment results. This paper looks at how machine learning could change oncology by focusing on how it can be used to find cancer early, predict how it will progress, and create personalized treatment plans. Early identification is very important for increasing the number of people who survive, but standard testing methods aren't always accurate or quick enough. Machine learning calculations can discover patterns and exceptions that conventional investigations might miss by utilizing enormous datasets from an assortment of sources, such as electronic wellbeing records, hereditary information, and imaging thinks about. We see at cutting-edge machine learning models, like deep learning neural systems, support vector machines, and gathering strategies, to see how well they work at finding diverse sorts of cancer prior. The paper discuss about some important examples of how these models have done much better than standard diagnostic tools, allowing for earlier treatments and better patient results. The study emphasize about how natural language processing (NLP) can be used to get useful information from unorganized clinical notes. This can help with diagnosis and give us a fuller picture of the patient's background and risk factors. The study also talks about how predictive models can be used to make treatment plans better. Machine learning models can help doctors adjust treatments to each patient's unique profile by looking at patient data and how they respond to treatment. This makes treatments more effective while reducing side effects. © 2024 IEEE.

关键词： Contrastive Learning

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Detection of Criminal Activity using Deep Learning

Detection of Criminal Activity using Deep Learning

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Purushotham, P. Srividya, Ganja Chitty, Avula Kurakula, Arun Kumar Silparaj, Manda Kiran, Ajmeera Department of Computer Science and Engineering MLR Institute of Technology Hyderabad500043 India Dept of CSE Stanley College of Engineering & Technology for Women Hyderabad India VR Siddhartha Engineering College SAHE Deemed to be University Vijayawada India Mohan Babu University Andhra Pradesh Tirupati India CVR College of Engineering Hyderabad501510 India

ISBN: (纸本)9798350376913

The surge in criminal activities presents a formidable challenge for global law enforcement agencies. Traditional crime detection methods often struggle to address the intricate and varied nature of criminal behaviors. Many businesses deploy CCTV systems for continuous observation of people and their activities. The developed system exhibits significant potential for applications in public safety, surveillance, and law enforcement, serving as an intelligent and automated tool for early detection and prevention of criminal activities. Continuous human monitoring of data is nearly impossible due to the workforce and attention required. Automation becomes essential. The project proposed utilizes the deep learning technology, a CNN model is designed to identify harmful behaviour in the videos. To expedite the identification of abnormal events, it is crucial to automate this process, indicating which frame and segment contain the unusual activity. This involves dividing the video into frames and scrutinizing the people and activities in each processed frame. When it detects criminal activity, it can trigger alerts or notifications to the personnel, allowing them to respond promptly. © 2024 IEEE.

关键词： Deep learning

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Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

Equity in Healthcare: Analyzing Disparities in Machine Learn...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Zainab Al-Zanbouri Gauri Sharma Shaina Raza Computer Science Dept. Toronto Metropolitan University Toronto ON Canada Dept. of Electrical & Computer Engineering McGill University Montreal QC Canada AI Engineering Vector Institute for Artificial Intelligence Toronto ON Canada

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This study investigates how machine learning (ML) models can predict hospital readmissions for diabetic patients fairly and accurately across different demographics (age, gender, race). We compared models like Deep Learning, Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes. GBM stood out with an Fl-score of 84.3% and accuracy of 82.2 %, accurately predicting readmissions across demographics. A fairness analysis was conducted across all the models. GBM minimized disparities in predictions, achieving balanced results across genders and races. It showed low False Discovery Rates (FDR) (6-7%) and False Positive Rates (FPR) (5%) for both genders. Additionally, FDRs remained low for racial groups, such as African Americans (8 %) and Asians (7 %). Similarly, FPRs were consistent across age groups (4 %) for both patients under 40 and those above 40, indicating its precision and ability to reduce bias. These findings emphasize the importance of choosing ML models carefully to ensure both accuracy and fairness for all patients. By showcasing effectiveness of various models with fairness metrics, this study promotes personalized medicine and the need for fair ML algorithms in healthcare. This can ultimately reduce disparities and improve outcomes for diabetic patients of all backgrounds.

关键词： Analytical models Accuracy Precision medicine Refining Merging Medical services Predictive models

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Improved ANFIS Model Based on Confinement Optimized PSO to Predict the Air Quality of Delhi, India

Improved ANFIS Model Based on Confinement Optimized PSO to P...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Aryan Karki Chaitanya Hardikar M Akshay Kumar Sujit Das Dept of Computer Science and Engineering National Institute of Technology Warangal India

This paper proposes an improved version of the adaptive neuro-fuzzy inference system (ANFIS) to forecast the air quality index in New Delhi, India. We have used confinement optimized particle swarm optimizer (COPSO) to improve the performance of ANFIS, where COPSO is an improved version of particle swarm optimizer (PSO). This proposed model has been trained with previous nine years air quality index time series data of Delhi city to forecast the air pollutants such as particulate matter (PM10), sulfur dioxide (SO 2 ) and carbon monoxide (CO) as these three air pollutants are found to have severe impact on the air quality of Delhi. We compare the performance of the proposed model (COPSO-ANFIS) with the performance of PSO-ANFIS, polynomial regression and decision tree-based models, where the result shows a significant improvement compared to other approaches.

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Synergistic Skin Cancer Classification: Vision Transformer alongside MobileNetV2

Synergistic Skin Cancer Classification: Vision Transformer a...

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Intelligent Technologies (CONIT), International Conference on

作者： M Ranjith Kumar S Priyanga Janani Srinivasan Anusha Veda Chatiyode Jerome Santiago P Revathi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Science and Engg. Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science and Engg. C. Abdul Hakeem College of Engg. and Tech. Melvisharam Vellore India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Skin cancer is one of the most common types of cancer in the world, and it poses major health risks due to its ability to spread quickly and metastasize. Early and accurate identification is crucial for treatment success and improved patient outcomes. This proposed work combines the MobileNetV2 and Vision Transformer (ViT) architectures to create a hybrid automated skin cancer classification technique. This technique aims to increase the accuracy and efficiency of dermatological diagnosis tools by combining MobileNetV2’s effective feature extraction capabilities with ViT’s self-attention mechanism. After testing on the HAM10000 dataset, this hybrid model outperformed individual models with a remarkable $96.3 \%$ classification accuracy. Not only did the integration of ViT and MobileNetV2 improve the classification accuracy but it also demonstrated how various deep-learning architectures work together to tackle complex image analysis tasks. Classifying skin cancers is critical to the medical industry because it allows for the early diagnosis of various dermatological disorders, allowing prompt intervention and treatment. These benefits can eventually improve patient outcomes and save lives in huge numbers. The findings of this research highlight the potential of deep learning to transform dermatological diagnostics and open the door to creating systems that will significantly impact clinical practice by detecting skin cancer with greater accuracy and efficiency.

关键词： Deep learning computer vision Accuracy Computational modeling computer architecture Transforms Transformers

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Identification and Segmentation of Polyp Using Deep Learning Approaches

Identification and Segmentation of Polyp Using Deep Learning...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Suman Yaligar Shanta S. Kallur Vijeeta Patil Puneeth N. Thotad Dept. of Computer Science and Engineering K. L. E. Institute of Technology Hubballi India Affiliated to Visvesvaraya Technological University Belagavi Dept. of Master of Computer Applications K. L. E. Institute of Technology Hubballi India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Colorectal cancer (CRC) is a major health problem, and accurate detection of polyps plays a major role in its prevention and treatment. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the rise in deep learning, more and more rigorous segmentation of medical images dependent on deep learning networks have emerged, making significant progress in this field. This paper aims to develop an efficient model for polyp detection and segmentation using machine learning techniques. The model will assist clinicians in accurately locating and segmenting polyp regions, which will help in the prevention and treatment of CRC. The work will first review traditional methods that are dependent manually on extracted features and deep segmentation algorithms, then detail benchmark datasets relevant for the topic. The two datasets Kvasir-SEG and CVC-ClinicDB are used for implementation. Specifically, we carried out a comprehensive evaluation of recent deep learning CNN methods. The model gave an accuracy with Pranet method of about 99% in polyp segmentation and identification respectively.

关键词： Deep learning Image segmentation Technological innovation Accuracy Shape Prevention and mitigation Colorectal cancer Feature extraction Convolutional neural networks Medical diagnostic imaging

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Performance Analysis of Machine Learning Algorithms to Predict Water Potability

Performance Analysis of Machine Learning Algorithms to Predi...

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Computational Intelligence for Information, Security and Communication Applications (CIISCA), International Conference on

作者： J Parnika Shridhar B Devamane C N Ramya M G Swarna Laxmi Dept. of Computer Science & Engineering Global Academy of Technology Bengaluru Karnataka India

The survival of all living organisms, industry and agriculture is dependent on freshwater availability. A crucial step in the management of freshwater is the examination of the water's quality. According to the annual report of the World Health Organization, a lack of safe drinking water is resulting in the illness or death of many people, particularly children and pregnant women. Before the water is used for any purpose, including chemical spraying (for pesticides, etc.), or drinking water, it is essential to test its quality. Finding water that is safe to consume can be done to some extent by testing the water's quality. Therefore, water quality testing is essential. In this paper, we have carried out the implementation of 3 machine learning algorithms to check for potability of water after balancing, selecting important features using the Univariate Selection method and standardizing the data. Random Forest Classifier gave the maximum accuracy of 88.75%. The main objective of our work is to increase the accuracy and demonstrate the performance analysis of the considered algorithms' accuracy in this paper along with the existing works.

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Midway Advertisement: A Mechanism to Curb Annoyance due to Unwanted Advertisements

Midway Advertisement: A Mechanism to Curb Annoyance due to U...

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2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Ur Rahman, Mohammed Ehsan Yelishetty, Aishwarya Kakatiya Institute of Technology and Science Dept. of Computer Science and Engg Warangal India

ISBN: (纸本)9781665435215

The current trends in viewers' behavior and attitudes while viewing an advertisement are suggestive of the fact that eventually, every viewer who can become annoyed due to an advertisement will be using advertisement blocking software and/or anti-Annoyance software as proposed in this paper. This paper presents an abstract, theoretically framed proposed work to avoid annoyance in viewers caused due to distracting digital advertisements and content, by customizing the response a viewer's device, such as mobile phones/desktops, can give while an advertisement is being displayed. The paper deals with the solutions to the emotional trigger due to advertisements, proposing a special hardware device that extracts information required for the ad-blocking or anti-Annoyance applications running on the device, and avoiding annoyance, displeasure, and lack of concentration in individuals by considering various human characteristics, like one's aesthetic sense, physical characteristics, entertainment taste, etc. Our research work also provides theoretical and quantitative analysis, proof of an intelligent and customizable system to prevent disturbance, distraction, anxiety, and other unnecessary emotional imbalances due to repeated online advertisements. The results show the real-world marketing effects on targeted people. The work also discusses to what degree the vexation and impatience levels vary with the ads containing different levels of product class and socioeconomic class. As blocking advertisements has a lot of psychological and financial implications on one's life, our work leaves an outlet for substantive investigation into innovative, high-quality marketing content, marketing strategies, and significant unseen effects on users and leaves a pathway for relaxing effects on the user through the proposed hardware and software, spanning a wide range of subject areas. © 2021 IEEE.

关键词： Marketing

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Multi-Task Learning for Sarcasm Detection and Sentiment Analsysis Using BERT

Multi-Task Learning for Sarcasm Detection and Sentiment Anal...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： B. Jyothsna Swathi Gowroju V. Sahithi Fahd Khadri B. Sri Vinny P. Vardhan Raj Dept of CSE (AI & ML) Sreyas Institute of Engineering and Technology Telangana India Computer Science Engineering Sreyas Institute of Engineering and Technology Telangana India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This paper proposes a Multi –Task Learning Model approach to detect Sarcasm and to analyze Sentiment within the given text. Sarcasm detection is important for enhancing natural language understanding particularly in customer reviews and social media where sarcastic sentences are mostly used. This system uses BERT (Bi-directional Encoder Representations from Transformers) to processes both tasks concurrently. It uses separate layers to handle each task individually. The text processing pipeline includes techniques such as text normalization, tokenization, and lemmatization and then followed by encoding using BERT’s tokenizer before using it for model training. BERT can understand the complex patterns of language which helps in easily recognize the small clues present in sarcastic statements. The architecture of the model incorporates specific layers for sarcasm and sentiment classification which optimizes prediction with loss functions for each task. If there is imbalance in class distributions class weighting is applied and model evaluation is performed using K-Fold cross validation to ensure robustness. To measure the model effectiveness performance metrics such as ROC-AUC curves, confusion matrix and detailed classification reports are used. Therefore, including MTL and BERT improves the performance and effectiveness in sarcasm detection and sentiment analysis.

关键词： Analytical models Sentiment analysis Social networking (online) Computational modeling Transfer learning Bidirectional control Predictive models Transformers Encoding Tokenization

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