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LSB based Image Steganography with Password-Protected SMTP Transmission

LSB based Image Steganography with Password-Protected SMTP T...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Mangali Bhanu Prakash E. Ravi Kumar Banavath Anitha Boda Bansilal Babu Reddy Mukkamall Gudipati Pranaya Sanjana Dept. of Information Technology Vardhaman College Of Engineering Hyderabad India Dept. of Computer Application Krishna University Machilipatnam Machilipatnam India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Picture steganography plays a crucial role in secure communication by discreetly altering images for various purposes such as data authentication, privacy protection, and watermarking. However, traditional methods often lack resistance to manipulation. To address this, we propose a solution that merges automated password generation with the Least Significant Bit (LSB) approach. This method involves embedding text within images and automatically generating an encryption password to safeguard data security. By employing Simple Mail Transfer Protocol (SMTP) credentials, encrypted photos can be directly sent to recipients, significantly expediting the process. This automated approach eliminates the necessity for separate transfers of the image and decryption password, thereby enhancing simplicity and security. This new technology seamlessly integrates encryption, ensuring a trustworthy and secure communication channel. With this innovative solution, users can transmit sensitive information confidently, knowing that it is shielded from unauthorized access or manipulation. By combining the strengths of automated password generation, LSB steganography, and SMTP transmission, our approach provides a robust framework for safeguarding digital communication in various context.

关键词： Resistance Steganography Protocols Transform coding Passwords Watermarking Streaming media Real-time systems Encryption Postal services

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Virtual Simulation based Visual Object Tracking via Deep Reinforcement Learning

Virtual Simulation based Visual Object Tracking via Deep Rei...

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2022 International Conference on Information Science and Communications Technologies, ICISCT 2022

作者： Farkhodov, Khurshedjon Park, Jin-Hyeok Lee, Suk-Hwan Kwon, Ki-Ryong Pukyong National Univercity Dept. of Artificial Intelligence Convergence Busan Korea Republic of Pukyong National University Dept. of It Convergence and Application Engineering Busan Korea Republic of Donga University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9781665472296

The current research field of object tracking has become noticeably popular among researchers where AI techniques take place with high-level accuracy. An algorithm with multifunctional abilities had proposed in different proposals in recent years. We proposed a tracking technique integrated with a virtual reality simulator - the AirSim (Areal Informatics and Robotics Simulation) City Environ model using one of the DRL models to control with a drone agent to examine a realistic environment. Additionally, the suggested method had tested via the two public: VisDrone2019 and OTB-100 datasets to compare with conventional strategies to show better performance among recent works. © 2022 IEEE.

关键词： Reinforcement learning

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and dept. of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Navigation: Enhancing Vehicle Trajectory Forecasting with Deep Learning

Smart Navigation: Enhancing Vehicle Trajectory Forecasting w...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Muneera Hashim Nadera Beevi S Dept. of Computer Application TKM College of Engineering APJ Abdul Kalam Technological University Kollam India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Methods for accurately predicting the trajectories of vehicles have always been sought after by Intelligent Transportation Systems (ITS) to improve travel efficiency and traffic management. Advances in deep learning and the availability of extensive trajectory datasets have made such predictions feasible with low error rates. The paper presents a vehicle trajectory prediction model by leveraging the power of stacked Long Short-Term Memory (LSTM) networks and a highly refined dataset. A novel approach for feature extraction and selection is introduced focusing on trajectory properties such as entropy, elongation and elongation angle. The most relevant predictors are identified through a combination of similarity measures like cosine similarity, Tversky index and Pearson correlation coefficient. The model demonstrates superior performance compared to existing methods achieving a low Mean Squared Error (MSE) of 0.004 and Mean Absolute Error (MAE) of 0.01. The model has potential applications in manual, hybrid and autonomous driving environments.

关键词： Deep learning Three-dimensional displays Transportation Predictive models Feature extraction Entropy Trajectory Indexes Elongation Long short term memory

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Analyzing and Detecting the Fake News using Machine Learning 2

Analyzing and Detecting the Fake News using Machine Learning

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Upadhyay, Santosh Kumar Singh Yadav, Manmohan Chaudhary, Vipin Kumar Rasool, Motashim Ajay Kumar Garg Engineering College CSE Department Ghaziabad India Sharda University CSE Department Greater Noida India Lovely Professional University CSE Department Phagwara India Integral University Dept. of Computer Application Lucknow India

ISBN: (纸本)9798350368109

Fake news has grown to be a significant concern in the age of social media and online information sharing. False news detection is a difficult problem requiring a multidisciplinary approach combining natural language processing, machine learning, and social network research. This paper provides a short review of the area by classifying the research on false news identification into three categories: feature-based approaches, machine learning methods, and social network analysis methods. Further, we have applied machine learning algorithms to identify false news. We look at various feature types used for false news identification, including textual features, metadata features, and social context features. Our proposed system performed well with a classification accuracy of 99.70%. Furthermore, we delineate prospective remedies for the issues and constraints of existing fake news detection methods. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT based dual health monitoring device for liquid infusion and urine level measurement of urine bag

IoT based dual health monitoring device for liquid infusion ...

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Communication, Control and Intelligent Systems (CCIS)

作者： Amit Kumar Pooja Pathak Vijay Kumar Dwivedi Dilip Kumar Sharma UPCST GLA University Mathura India Dept. of Computer Engineering and Application GLA University Mathura India Dept. of Mechanical Engineering GLA University Mathura India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Advanced healthcare monitoring devices that improve patient care through real-time data collecting, remote monitoring, and individualized therapy have been developed as a result of the integration of IoT technology in the healthcare industry. Vital indications including heart rate, blood pressure, glucose levels, and oxygen saturation may be continuously monitored thanks to these IoT-enabled gadgets, which include wearable sensors, smart implants, and remote monitoring systems. These devices assist in the early detection of potential health issues, enabling timely intervention. They help reduce hospital readmission rates by monitoring patients remotely. Additionally, they enhance chronic disease management by transmitting real-time data to healthcare specialists. Furthermore, by providing feedback and reminders, IoT-based healthcare devices encourage patient participation and adherence to treatment programs. Despite the many advantages, IoT technology's role in healthcare monitoring is about to change patient outcomes as it develops. The progress in technology over the years has made it possible to use smaller devices, such as smartwatches mobiles, for health monitoring and disease detection.

关键词： Liquids Circuits Medical services Real-time systems Glucose Level measurement Biomedical monitoring Remote monitoring Wearable sensors Diseases

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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Transformer Based Classification of Closely Related Dravidian Languages in Code-Mixed Data

Transformer Based Classification of Closely Related Dravidia...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Anandhu Saji Fousia M Shamsudeen Centre for Artificial Intelligence TKM College of Engineering Kollam Kerala India Dept. of Computer Application TKM College of Engineering Kollam Kerala India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

In recent years, code mixing, the blending of words and phrases from multiple languages within a single conversation has become increasingly common in multilingual contexts, particularly in India. This work investigates the effectiveness of transformer based models in identifying languages within code mixed data that includes English and three closely related Dravidian languages: Malayalam, Kannada, and Tamil, all written in English script. A comprehensive dataset was developed with five distinct labels: Tn-en (Tamil-English), Kn-en (Kannada-English), Ml-en (Malayalam-English), and En (English). Transformer models, including BERT, CamemBERT, mBERT, DistilBERT, and ELECTRA, were employed for classification, with CamemBERT achieving the highest validation accuracy of 97.62% due to its ability to handle morphologically rich languages effectively. The findings highlight the potential of transformer models in enhancing natural language processing (NLP) applications for code-mixed text, particularly in managing the complexities of closely related multilingual environments.

关键词： Performance evaluation Codes Accuracy Tagging Transformers Data models Real-time systems Multilingual Complexity theory Speech to text

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An Effective Classification of Ddos Cloud Based Attack Through Tree Founded Classifiers 1

An Effective Classification of Ddos Cloud Based Attack Throu...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Goel, Vikas Goel, Pragati Ranjan, Raju Sharma, Amit Kumar KIET Group of Institutions Delhi-NCR Dept. of IT Ghaziabad India NCRD'S Sterling Institute of Management Studies Nerul Dept. of Computer Application Navi Mumbai India School of Computing Science & Engineering Galgotia University Greater Noida India Ch. Charan Singh University Campus Dept. of CS&E Meerut India

ISBN: (纸本)9798350345957

The term 'cloud computing' refers to a relatively new technology that provides users with access to various computing services and resources, including software, data storage, and communication infrastructure. There are currently certain security concerns with cloud computing. An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DoS attacks, which aim to disrupt, disable, or otherwise harm a service, have become increasingly common against cloud applications and internet services. Due to the cloud's complexity and decentralized nature, it is difficult to detect attacks on its infrastructures. Moreover, by allowing a wide variety of smart devices to access cloud-based infrastructures, it raises both the difficulty and the complexity of assaults. A decision tree is a method of categorization used in data mining. It is recommended to utilize this method when developing a classification scheme from a reclassified data set. Classifiers such as the Classification and Regression Tree (CART), C4.5, and the Random Tree (RT) are proposed in this work. The utilized CART method has a tendency to generate additional trees on nodes, until no tree can be formed judging whether or not a node is terminal. Because of its adaptability, the CART technique can handle gaps in the data. The tree is pruned to make it smaller and more manageable in C4.5. A classifier's complexity is decreased while its prediction accuracy is improved. Decision trees were used to generate the chain of basic decisions used in RT's label assignment process. © 2023 IEEE.

关键词： Decision trees

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Aashdeep Singh Sakshi Dhawan Jyoti Verma Gurpreet Singh Amanpreet Kaur Dept. of Computer Science & Engineering Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Rajpura Punjab India Dept. of Computer Application Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Rajpura Punjab India Dept. of Computer Science & Engineering Chitkara University Institute of Engineering & Technology Rajpura Punjab India

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning.

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