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An Efficient Detection of Floating Marine Debris using Deep Learning Techniques

An Efficient Detection of Floating Marine Debris using Deep ...

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2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Pandey, Prachi Guru Prasad, M.S. Mayank, Kumar Gowda, Mahima M Prashanth Kumar, S.P. Karthik, D.U. Dept. of Computer Science and Engg. Dehradun India REVA University School of Computing and Information Technology Bengaluru India Acharya Institute of Technology Dept. of Computer Science and Engg. Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350344509

Growing concerns about the environmental consequences of floating debris in aquatic ecosystems have underscored the need for the development of efficient and automated methods for debris classification and monitoring. This research paper presents a comprehensive investigation into the application of neural networks, specifically Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM architectures, for this purpose. Our study entails the creation of a diverse dataset encompassing various debris types and environmental conditions, ensuring its real-world relevance and generalizability. Thorough exploration is conducted of the three neural network models to evaluate their effectiveness in classifying floating debris from images captured in aquatic environments. CNNs are chosen for their established image recognition capabilities, while LSTMs are incorporated to capture sequential information and potential temporal dependencies within debris trajectories. The outcomes of our study unveil the strengths and limitations of CNN, CNN-LSTM, and LSTM architectures in the context of floating debris classification. Proposed work provides insights into the suitability of each model in various real-world scenarios, including riverine systems, oceans, and coastal regions. Furthermore, we discuss the implications of our findings for environmental monitoring, debris removal strategies, and policy development. Throughout our research, each model's performance is assessed in terms of accuracy, precision, recall, and F1-score, considering the unique challenges posed by the inherently noisy and dynamic nature of aquatic environments. This research contributes to the evolving body of knowledge regarding the application of neural networks in environmental monitoring, with a particular focus on the critical domain of floating debris classification. Our findings are expected to guide the development of automated systems that can aid in mitigating the environmental i

关键词： Debris

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LSB based Image Steganography with Password-Protected SMTP Transmission 2

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

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

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

ISBN: (纸本)9798350388916

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. © 2024 IEEE.

关键词： Authentication Protocol

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Harnessing Big Data for Economic Transformation in a Digitalized World 3

Harnessing Big Data for Economic Transformation in a Digital...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Tarigan, Riswan Efendi Triyono Mahyuni Agustian, Harry Nurfaizi, Rifky Zahra, Achani Rahmania Az Pelita Harapan University Dept. of Information Systems Tangerang Indonesia University of Raharja Dept. of Information Technology Tangerang Indonesia University of Mataram Dept. of Teacher Training and Education Science Mataram Indonesia Sesindo Dept. of Computer Systems Tangerang Indonesia Bosindo Dept. of Information Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Information Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

Air is essential to life and health, with oxygen (20.94%) and the ozone layer protecting against ultraviolet radiation. Depletion of oxygen reduces the ozone layer, increasing UV exposure. Good air quality is crucial for living organisms. In 2022, Indonesia's PM 2.5 pollution ranked 26th worst globally, exceeding WHO standards, yet awareness and trust in air quality devices remain low. This study aims to implement and evaluate an AI-based machine learning system to predict the Air Quality Index (AQI) in healthcare services. Using Structural Equation Modeling with Partial Least Square analysis via SmartPLS 4.0, the research involves 120 participants and historical air quality data. Comparative analysis of machine learning algorithms assesses accuracy, robustness, and efficiency, highlighting their potential in forecasting AQI and its implications for public health and urban planning. The study demonstrates the effectiveness of these algorithms in predicting AQI, with a significant value of 0.874 for forecasting accuracy. © 2024 IEEE.

关键词： Ozone layer

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Enhancing Network Intrusion Detection in a Limited Labeled Data Scenario: An Active Learning Approach

Enhancing Network Intrusion Detection in a Limited Labeled D...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Md Sharafat Hossain, Md Al Amin Sikdar, Md Shahin Islam, Md Saiful Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka Bangladesh Selcuk University Dept. of Information Technology Engineering Konya Turkey Prime University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The Internet was originally developed to facilitate easier communication through digital devices. It has made our lives easier by providing means for communication and delivering digital resources anywhere and at any time in the world. However, the internet also exposes users to the risk of criminals exploiting various vulnerabilities in the internet system, launching sophisticated attacks to gain valuable information and hamper users' lives. Henceforth, an effective intrusion detection system (IDS) is imperative for safeguarding the data and resources of users in the cyber world. This research aims to enhance semi-supervised IDS models by leveraging limited labeled data and unlabeled data and exploring innovative approaches to improve adaptability and accuracy in detecting known and emerging threats, particularly in scenarios with limited labeled data available. Our study uses the UNSW-NB15 dataset, and the results show that our suggested active learning-based semi-supervised model, which was created with 2000 labeled data, is better than the supervised model that uses the same number of labeled data. The proposed model achieved an accuracy score of 97.67% while existing studies achieved an accuracy score of 94.61%. © 2024 IEEE.

关键词： Network intrusion

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5G the Missing Piece in Digital Transformation of the Construction Sector 28

5G the Missing Piece in Digital Transformation of the Constr...

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28th International computer Science and Engineering Conference, ICSEC 2024

作者： Chnaidini, Shaik Khan, M. Jebran Ullah, Fasee Khan, Arafat Ahmad Prashar, Deepak Gulf American University Dept. of Civil Engineering Fujairah United Arab Emirates Sarhad University of Science and Information Technology Dept of Computing and Information Technology Peshawar Pakistan Uinversiti Teknology Petronas Dept. of Computer and Information Sciences Seri Iskandar Malaysia Khonkaen University Dept. of Computer Science Khon Kaen Thailand Lovely Professional University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350366860

The 5th generation of wireless technology with a continuum leap rushing towards the future in the world of communication has a puzzle that enables the autonomous machine to work in site making a louder voice that shows the era where even if we cannot be fully robotic, we can make the machines do the work by sitting on a faraway control room. Making ways clear to see the work done miles away on construction sites the 5th generation has not only brought the people close through communication but also made them work collegiately with a clearer view front. The paper discusses 5G and the other related technologies that are emerging all over the world and how it is needed specifically in the construction industry. In the present paper, we tried to shed light on the acceptance and implementation of 5G technology in the construction sector. It is seen that the construction sector in India is lagging behind when it comes to implementation and availability of required support and resources for the 5G implementation. © 2024 IEEE.

关键词： Construction industry

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Preliminary Analysis of Programming Exercise Logs to Provide Effective Feedback to Students 14

Preliminary Analysis of Programming Exercise Logs to Provide...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Tanaka, Tetsuo Shrestha, Sujan Miyazaki, Tsuyoshi Okazaki, Hidetoshi Nakakawaji, Nozomi Ueda, Mari Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan Kanagawa Institute of Technology Course of Information and Computer Sciences Graduate School of Engineering Atsugi Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information Media Atsugi Japan

ISBN: (纸本)9798350324228

The authors have developed a programming exercise system and are already using it in classes. This system provides students with a web-based programming environment, and monitor the time that students worked on exercises, logging operations such as program editing, building, execution, and testing, and their results. For teachers, it also shows how each student is progressing, the source code creation process, and the occurrence of errors. Teachers' evaluation of these functions is beneficial, but how feedback is given to students is inadequate. In this study, to examine feedback to students, we do a preliminary analysis of the operating log of this system. We used this system in a programming class of about 230 students for 15 weeks, collecting thousands to 100,000 log events such as the number of keystrokes, the number of working hours, the number of exercise programs completed, the number of copies and pastes, etc. per day. We can summarize our results as follows: 'The greater the number of keystrokes, working hours, and program completions in class, the better the results';'The number of keystrokes after the class ends (after the answer example has been published) does not affect the grades', and similar conclusions. In addition, based on these findings, we can identify conditions that increase the probability of becoming a poor performer. © 2023 IEEE.

关键词： Students

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Explaining Instructional Factors Affecting M-Leaming Using the Moderator of Social Media: An Extension of the IS and TAM Models 7

Explaining Instructional Factors Affecting M-Leaming Using t...

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7th International Conference on E-Society, E-Education and E-technology, ESET 2023

作者： Bamaga, Arwa Zafar, Bassam Terzis, Sotirios King Abdulaziz University Jeddah Dept. of Computer and Information Technology Saudi Arabia University of Strathclyde Glassgow Dept. of Computer and Information Science United Kingdom

ISBN: (纸本)9798350344608

information and communication technology (ICT) is a term which refers to the incorporation of electronic technologies to manage knowledge and information. It has transformed the traditional education system into an efficient and flexible e-learning context. E-learning enables the higher-education institutions to create a traditional-like virtual classroom. The universities must stay abreast with the current technology to evaluate the quality factors that affect the e-learning process. This paper presents a theoretical study of the quality factors affecting e-learning in the mobile environment and presents two research contributions. First, we propose a model which suggests to add social media as a moderator that effects the relationship between user's satisfaction and user's intention-To-use. Second, we propose to add instruction quality attribute to the existing variables of well-recognized TAM and Extended IS models which enhance the e-learning experience within the mobile environment in higher education institutions in Saudi Arabia. © 2023 IEEE.

关键词： E-learning

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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A Hybrid Homomorphic Model with RSA Algorithm and Modified Enhanced Homomorphic Encryption Technique

A Hybrid Homomorphic Model with RSA Algorithm and Modified E...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Kamatchi, T.P. Anitha Kumari, K. PSG PTC. Dept. of Computer Computer Networking India PSG CT. Dept. of Information Technology India

ISBN: (纸本)9798350335835

Security for sending secret information over the network is the most demanding in today's reality for a wide range of applications. Many malicious attacks target confidential information. As a result, securing sensitive information on the Internet has become a serious challenge. Cryptographic algorithms are the most secure option when it comes to protecting against malicious assaults. information can be safeguarded using a variety of approaches. Another big difficulty that still needs to be explored is safeguarding information in the cloud. Thus, the concept of homomorphic encryption technique arises to safely process the data in the encrypted format rather than on plain text. There are a variety of cryptography techniques, but this system utilizes Homomorphic Encryption to combine the new technique with the RSA algorithm, allowing us to conduct arbitrary operations on RSA while maintaining the security of the existing methodology. Our main goal in this system is to assure data security, so a new model suggested a method based on the RSA algorithm and Modified Homomorphic Encryption. © 2023 IEEE.

关键词： Smart city

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Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction 12

Addressing Weight Competition in Long Short-Term Memory (LST...

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12th International Conference on Awareness Science and technology, iCAST 2023

作者： Lin, Kun-Wei Li, Yu-Fan Lin, Che-Yu Jiang, Zheng-Han Chen, Wei-Jian dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This conference paper presents a study on the application of Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of temperature change forecasts. The effectiveness of these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of mitigating weight competition to achieve more reliable and accurate temperature predictions. © 2023 IEEE.

关键词： Long short-term memory

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