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Integrating IoT and Blockchain-Enabled Device-to-Device Communication to Improve Privacy in Intelligent Quarantine Environments 7

Integrating IoT and Blockchain-Enabled Device-to-Device Comm...

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7th IEEE International Conference on Computational systems and information technology for Sustainable Solutions, CSITSS 2023

作者： Siddiqui, Shams Tabrez College of Computer Science and Information Technology Jazan University Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350343144

The exponential progress of Internet of Things (IoT) technology has resulted in the emergence of groundbreaking solutions across diverse industries, such as healthcare and quarantine management. In the realm of global health crises, such as the ongoing COVID-19 pandemic, the successful execution of efficient quarantine protocols assumes paramount importance. The objective of this research paper is to examine the potential integration of Internet of Things (IoT) devices, wireless sensors, and blockchain technology in order to establish a Smart Quarantine Environment (SQE). The SQE aims to improve privacy, security, and efficiency in the management of quarantine protocols. By analyzing the existing literature and exploring relevant case studies, this paper seeks to provide insights into the feasibility and benefits of implementing such a system. The utilization of IoT devices and wireless sensors enables the facilitation of real-time monitoring and data collection. This approach is supported by the integration of blockchain technology, which ensures data integrity, transparency, and user control. By addressing concerns related to privacy and security, this combination of technologies enhances the overall effectiveness of the system. © 2023 IEEE.

关键词： Internet of things

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Reversible Data Hiding in Shared Images Based on Syndrome Decoding and Homomorphism

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IEEE Transactions on Cloud Computing 2023年第3期11卷 3085-3098页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing210044 China Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education CICAEET Jiangsu Nanjing210044 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974 Taiwan Fudan University School of Computer Science Shanghai200433 China

Reversible Data Hiding in Encrypted Images (RDHEI) has drawn increasing concern in multimedia cloud computing scenarios. It embeds secret message into the encrypted carrier while preserving the confidentiality of the image. However, most RDHEI schemes have only one hider and one image carrier which are not efficient for the distributed system with multiple participants. Moreover, if the stego-image is lost, the cover image and the embedded data cannot be restored. To solve above issues, this paper proposes Reversible Data Hiding in Shared Images based on syndrome decoding and homomorphism (RDHSI). In RDHSI, the cover image and secret data are distributed using secret sharing. Multiple data hiders embed shared data into the shared images based on the Syndrome Decoding of Hamming codes and the additive homomorphism of the polynomial. On the receiver side, the lossless cover image and secret data are obtained. The reversibility and privacy preserving of the cover image, the complete extraction of secret data, and the fault-tolerance of the proposed schemes are achieved. The experimental results show that PSNR of the marked image is above 54 dB at the embedding rate of 0.43 bpp. In order to improve the payload of secret data, the Modified RDHSI is further proposed. Generally, the proposed schemes are secure, effective and fault-tolerant. © 2013 IEEE.

关键词： Data privacy

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches 14

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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14th International Conference on Software technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Ganga, Krishna INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool. © 2024 IEEE.

关键词： Lung cancer

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computer Generated Imagery Animation and Visual Effects Education in India during the Covid-19 pandemic: A significant assessment 2

Computer Generated Imagery Animation and Visual Effects Educ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raturi, Vinod Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Maharishi Maharishi University of Information Technology Lucknow India

ISBN: (纸本)9798350305418

The COVID-19 outbreak has been designated an epidemic in India. Lockdown was implemented on March 25, 2020 to fight COVID-19, affecting the country's school system. It has transformed conventional education into an Ed Tech paradigm where teaching and evaluation are done online. This research tries to highlight the challenges Animation and Visual Effects (VFX) educators experience while teaching and assessing online in India. This study used interpretive phenomenological analysis (IPA). Educators from public and private universities were surveyed. Animation and VFX Online instructors and evaluators were semi-structured. The research identified different types of difficulties experience while teaching and assessing online. Lack of basic amenities, external distractions, family disruptions during instruction and evaluation were key difficulties mentioned. Institutional support hurdles included training, technical assistance, and clarity, lack of understanding of online teaching platforms, In addition to a lack of technical understanding was came out from the research. That's good news for institutions that plan to frequently utilize online computer-Generated Imagery (CGI) Animation and Visual Effects (VFX) education and evaluation in the future. © 2023 IEEE.

关键词： animation computer generated imagery (CGI) COVID-19 educator online education outbreak visual effects (VFX)

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Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2

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PeerJ computer science 2025年 11卷 1-22页

作者： Saxena, Pranshu Singh, Sanjay Kumar Rashid, Mamoon Alshamrani, Sultan S. Alnfiai, Mrim M. School of Computer Science Engineering and Technology Bennett University Greater Noida India University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information Technology College of Computers and Information Technology Taif University Taif Saudi Arabia

Background: Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes. Methods: This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 dataset and compared with VGG11 and SqueezeNet1_1, demonstrating modified ResNet-34’s optimal balance between depth and performance. FastAI-2 enhances computational efficiency, enabling rapid convergence with minimal training time. Results: The proposed system achieved 99.78% accuracy, confirming the effectiveness of automated lung cancer histopathology classification. This study highlights the potential of artificial intelligence (AI)-driven diagnostic tools to assist pathologists by improving accuracy, reducing workload, and enhancing decision-making in clinical settings. Copyright 2025 Saxena et al.

关键词： Deep learning

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Centralized Platform for Sensor Integration and AI Models in Precision Agriculture 2

Centralized Platform for Sensor Integration and AI Models in...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Chowdary, Ch Srinivasa Akshith Likith, S. Maya, B.S. Lokesh, S.V. Manish, Y. Asha, T. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India Bangalore Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A centralized platform for integrating various sensors and AI models in precision agriculture. The platform connects soil moisture sensors with AI models to generate crop recommendations and irrigation alerts, aiming to enhance resource utilization and crop yield. Key outcomes include real-time monitoring, actionable insights, and data-driven decision support for farmers. The practical implications for farmers and the agricultural industry are profound, as this system enables them to adopt precision agriculture techniques, reducing waste, increasing efficiency, and ultimately improving profitability © 2024 IEEE.

关键词： Smart agriculture

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Performance Analysis of RouteSegmentation Algorithm in Identifying Perimeter Area Around Travel Route 16

Performance Analysis of RouteSegmentation Algorithm in Ident...

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16th International Conference on information technology and Electrical Engineering, ICITEE 2024

作者： Bachmid, Muhammad Avied Anshari Haq, Muhammad Yasir Mumtaz, Muhammad Rafly Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Informatics Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Malang Indonesia Universitas Brawijaya Faculty of Computer Science Media Game and Mobile Laboratory Department of Information System Malang Indonesia

ISBN: (纸本)9798350375817

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency. © 2024 IEEE.

关键词： Transportation routes

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Beyond administrative reports: a deep learning framework for classifying and monitoring crime and accidents leveraging large-scale online news

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Neural Computing and Applications 2025年第10期37卷 7183-7205页

作者： Tuarob, Suppawong Tatiyamaneekul, Phonarnun Pongpaichet, Siripen Tawichsri, Tanisa Noraset, Thanapon Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Department of Computer Science University College London London United Kingdom Puey Ungphakorn Institute for Economic Research Bank of Thailand Bangkok Thailand

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers. © The Author(s) 2025.

关键词： Deep learning

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Advance Path Loss Model for Distance Estimation Using LoRaWAN Network's Received Signal Strength Indicator (RSSI)

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IEEE Access 2024年 12卷 83205-83216页

作者： Vo, Hoang Hoang Long Nguyen, Van Tran, Van Lic Ferrero, Fabien Lee, Fang-Yi Tsai, Meng-Hsun The University of Danang-University of Science and Technology Faculty of Advance Science and Technology Da Nang550000 Viet Nam The University of Danang-University of Science and Technology Department of Electronic and Telecommunication Engineering Da Nang550000 Viet Nam LEAT CNRS Universite Cote d'Azur Sophia Antipolis06903 France National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications. © 2013 IEEE.

关键词： Kalman filters

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Assessing the Impact of Drink and Driving on Fatalities in the United States using the Fatality Analysis Reporting system Database 8

Assessing the Impact of Drink and Driving on Fatalities in t...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Baryab, Mohammad Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Hasan, Abdullah Information Technology University of Punjab Department of Economics Lahore Pakistan Bahria University Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Computer Science and Information Technology Pakistan

ISBN: (纸本)9798350334012

To examine the impact of drinking and driving-related motor vehicle crash on fatalities in the US. The paper used cross-sectional data of the US covering 50 states, District of Columbia and Puerto Rico of 2020. The paper used linear regression to estimate the likelihood of death from drinking and driving-related motor vehicle crashes. Some age groups have also been made to check which age groups have a likelihood to die from drinking and driving-related motor vehicle crashes in the US. Moreover, it was also analyzed which sex was more involved in drinking and driving-related crashes of motor vehicles and that it resulted in their death. Historical violation and its relationship with drinking and driving-related motor vehicle crashes have also been examined. The results show Drink and drive has a positive relationship with deaths The study shows a positive relationship between drinking and driving with death from a drinking and driving-related motor vehicle crash. Some preventive measures such as check-posts should be there to stop or reduce prevalence of drinking and driving. © 2023 IEEE.

关键词： Accidents

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