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Visible Watermark Removal with Deep Learning Technology 6

Visible Watermark Removal with Deep Learning Technology

引用

6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Lin, Chia-Chen Wang, Pei-Yu Lin, Yan-Heng Huang, Hsuan-Chao Saberikamposhti, Morteza National Chin-Yi University of Technology Dept.of Computer Science and Information Engineering Taichung Taiwan Universiti Kebangsaan Malaysia Dept. of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350301953

Watermarking is a technique used to assert ownership over an image, and can be categorized into visible and invisible forms based on the detectability of the watermark. Visible watermarking is more user-friendly and intuitive than invisible methods since it allows individuals to identify image ownership with their own eyes rather than relying on machine-based watermark decoders. To enhance the visual quality of watermarked images and ensure the original images can be fully recovered after visible watermark authentication, a visible watermark removal approach using deep learning-based inpainting is proposed in this paper. Experimental results demonstrate that the watermarked images carrying the visible watermark and auxiliary information achieve peak signal-to-noise ratios (PSNRs) ranging from 41.89 dB to 43.17 dB and structural similarity indices (SSIMs) up to 0.97 to 0.98. Furthermore, our hybrid recovery operations enable the complete restoration of the original images, making them easily readable. © 2023 IEEE.

关键词： Watermarking

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Computation of Big Data Security through Cross-Layer Techniques 5

Computation of Big Data Security through Cross-Layer Techniq...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Priya Dwivedi, Shweta Bhati, Nitesh Singh Kumar, Santosh Sharda University Dept. of Computer Science Greater Noida India Integral and Innovative of Sustainable Education College Dept. of Information Technology Lucknow India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Because smart devices are so common and have generated enormous volumes of data, a new strategy is required to handle the kinds of data known as big data in the modern world. Many people are quite concerned about security because of the size of big data. Therefore, encryption is the best way to secure data;nevertheless, typical procedures are insufficient to fully protect huge data sets. Therefore, specific, and effective security algorithm solutions are required to protect large amounts of data. It becomes problematic when a sizable amount of the data is not encrypted and there is uncertainty about the data. Therefore, a cross-layer security architecture is offered in this research to secure the important component of the data. Numerous data security approaches are employed to safeguard data, ensure that crucial information is not lost, and encrypt it using a robust encryption algorithm. Based on encryption time and throughput, a comparison is conducted between the cross-A-coin approach and the suggested framework. Throughput is used to calculate performance based on encryption time, followed by full encryption. © 2024 IEEE.

关键词： Big Data Cross-Layer Techniques Security Algorithm and Big Database Reliability

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Customer Segmentation Using Hierarchical Clustering 9

Customer Segmentation Using Hierarchical Clustering

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Afzal, Areeba Khan, Laiba Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan Information Technology University Lahore Department of Computer Engineering Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350394474

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled 'MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING' as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall's customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry. © 2024 IEEE.

关键词： Consumer behavior

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Reducing Client-Side Cost in Secure Inner Product Evaluations 39

Reducing Client-Side Cost in Secure Inner Product Evaluation...

引用

39th International Conference on information Networking, ICOIN 2025

作者： Hahn, Changhee Koo, Dongyoung Hur, Junbeom Seoul National University of Science and Technology Dept. Electrical and Information Engineering South Korea Hansung University Dept. Convergence Security South Korea Korea University Dept. Computer Science and Engineering South Korea

ISBN: (纸本)9798331506940

In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption (IPFE), which enables the computation of inner products on encrypted vectors without exposing the vectors themselves. However, the computational intensity of IPFE decryption poses challenges, particularly for resource-limited client devices and high-dimensional vectors. To facilitate partial decryption, prior IPFE schemes require the client to derive a partial decryption key from an assigned secret key, incurring computation costs linear to the vector dimension. In this paper, we provide a framework for the challenges of IPFE, enabling efficient decryption outsourcing, maintaining data privacy, and facilitating key generation outsourcing in IPFE schemes. In the proposed framework, outsourced decryption using a partial decryption key generates a masked plaintext that only the client can unmask. Furthermore, the process of deriving the partial decryption key can be fully outsourced, thereby ensuring that the client's computational burden remains constant. © 2025 IEEE.

关键词： inner-product outsourced computation Security

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

引用

3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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An IoT-based Parameter Extraction Platform for Powder Metallurgy Sintering Furnace 11

An IoT-based Parameter Extraction Platform for Powder Metall...

引用

11th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2024

作者： Chang, Shih-Man Lin, Hao Pu Han, Chin-Chuan Wu, Yu-Chi National United University Dept. of Computer Science and Information Engineering Miao-Li Taiwan National United University Program in Material and Chemical Engineering MiaoLi Taiwan National United University Dept. of Electrical Engineering Miao-Li Taiwan

ISBN: (纸本)9798350366501

Internet of Things (IoT) technology has widely used in industrial production. Most cases focused on designing new equipment or processes. However, small and medium enterprises especially the powder metallurgy manufacturers in Taiwan would not replace existing production equipment due to the finical problems. In this study, an IoT-based platform is conducted to extract the sintering parameters for technology to augment sintering furnaces by installing sensors and data transmission modules, setting a sintering parameter extraction platform for powder metallurgy sintering furnace. Long-term monitoring and remote control are the main technologies for solving three problems during the sintering process, i.e., temperature, atmosphere, and time. Monitoring targets include checking whether the heaters are damaged, ensuring stable air flow meter readings, and verifying the normal speed of the conveyor belt. Additionally, remote control technology has been developed to execute cooling and reheating processes on the sintering furnace. The staying time on-site for personnel is reduced and the sintering time is increased for production increase during working hours. © 2024 IEEE.

关键词： Belt conveyors

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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

引用

6th International Conference on Electrical engineering and information and Communication Technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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Optimizing Web Server Performance: A Comparative Analysis of Central Manager and Round Robin Load Balancing Algorithms 4

Optimizing Web Server Performance: A Comparative Analysis of...

引用

4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Permana Arrasyid, Fahmi Stiawan, Deris Afifah, Nurul Heryanto, Ahmad Azhar, Iman Saladin B. Bardadi, Ali Yudho Suprapto, Bhakti Pahendra Anto Saputra, Iwan Sriwijaya University COMNETS Rerearch Group Faculty of Camp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia Sriwijaya University Faculty of Engineering Electrical Engineering Dept. Palembang Indonesia

ISBN: (纸本)9798350368253

Central manager algorithm has a lower average throughput compared to the round robin algorithm. This is evidenced by a significant decrease in throughput values during measurements. Central manager algorithm provides a faster average response time than round robin algorithm, despite experiencing an increase in response time values over time. Central manager algorithm shows a higher request loss rate and many requests are not served. Under normal performance conditions, both algorithms yield the same average request loss rate, achieving stable values without failed requests. The central manager algorithm ensures relatively balanced resource availability, including CPU and memory usage, across each slave web server compared to the round robin algorithm. The results show that increasing the number of clients and client requests during system testing to better observe server resource availability and load distribution performance. For future research, it is recommended to increase the number of clients in the system testing to enhance server load and to introduce client request actions on the web server to further highlight resource availability on each web server. © 2024 IEEE.

关键词： Response time (computer systems)

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PureConnect: A Localized Social Media System to Increase Awareness and Connectedness in Environmental Justice Communities 24

PureConnect: A Localized Social Media System to Increase Awa...

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11th Multidisciplinary International Social Networks Conference, MISNC 2024

作者： Hammad, Omar Rahman, Md Rezwanur Kanugo, Gopala Krishna Vasanth Clements, Nicholas Miller, Shelly Mishra, Shivakant Sullivan, Esther King Fahd University of Petroleum and Minerals Information and Computer Science Dept. Dhahran Saudi Arabia University of Colorado Boulder Computer Science Dept. BoulderCO United States University of Colorado Boulder Mechanical Engineering Dept. BoulderCO United States University of Colorado Denver Sociology Dept. DenverCO United States

ISBN: (纸本)9798400717550

Frequent disruptions like highway constructions are common now-a-days, often impacting environmental justice communities (communities with low socio-economic status with disproportionately high and adverse human health and environmental effects) that live nearby. Based on our interactions via focus groups with the members of four environmental justice communities impacted by a major highway construction, a common concern is a sense of uncertainty about project activities and loss of social connectedness, leading to increased stress, depression, anxiety and diminished wellbeing. This paper addresses this concern by developing a localized social media system called PureConnect with a goal to raise the level of awareness about the project and increase social connectedness among the community members. PureConnect has been designed using active engagement with four environmental justice communities affected by a major highway construction. It has been deployed in the real world among the members of the four environmental justice communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureConnect is potentially useful in improving community members' wellbeing and the members appreciate the functionalities it provides. © 2024 ACM.

关键词： Civic Engagement

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A Recommendation System for Crop Prediction Under Diverse Weather Conditions 9

A Recommendation System for Crop Prediction Under Diverse We...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Shunmugalakshmi, P. Ram Kishore, V. Ramachandran, Pillai Pratham Santhi, S. Kalaiselvi, S. National Engineering College Dept. of Information Technology Tamil Nadu Kovilpatti628503 India National Engineering College Dept. of Computer Science and Engineering Tamil Nadu Kovilpatti628503 India

ISBN: (纸本)9798350397376

In order to develop strategic initiatives in agricultural belonging for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computable and commercial decision at the territory plane. Crop production forecasting, which is done to foresee a greater crop output, is the impressive difficulty in the agronomic division. To help farmers increase agricultural yield, this study has collected and evaluated data on N, P, K, temperature, humidity, ph, rainfall and soil-moisture. The superior values of this work are to recommend the crop to cultivate, suggest the appropriate fertilizers to use and predict the disease of a crop. A python environment is used to perform initial preprocessing on the data. KNN classification is used for developing the recommendation system. The link between the count, nutrients, temperature, humidity, pH, rainfall, and crop, are visualized using bar graphs and scatter plots. The tensor and torch were used to forecast crop disease and also to suggest appropriate fertilizer based on the data. The application was developed using Python Flask. This Classification was compared with other algorithms such as SVM, DT , RF and K-means. © 2023 IEEE.

关键词： Support vector machines

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