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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Shakila, M. Saveetha School of Engineering SIMATS Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331530013

The application of blockchain technology management in the abstracts of the aforementioned studies, the process of supply chain. The paper emphasises how applying blockchain technology may enhance the transaction between stakeholders, as mentioned in the abstracts. In decentralisation systems in building dApp for sustainable food supply systems for security and traceability. The research highlights how critical it is for the agricultural supply chain to have open communication, transparency, and data integrity, and how blockchain technology is beneficial. Blockchain technology has the potential to enable small-scale farmers, increase profitability, and open up new markets, according to the papers. The beneficial effects on lowering food loss and accomplishing sustainable development, the main aim is to develop and enhance the agro-cultivation field to make a more efficient supply chain. The more we come across the emerging technique to be manifested in developing the agriculture dApp. The need for the development in eradicating the methodology of simplifying the anonymous entry of the business. Agricultural supply chain's traceability, efficiency, safety, and transparency. The monitoring and tracing of crops, as well as the recording and validation of transactions between supply chain participants, are discussed in the system using smart contracts and the Ethereum blockchain frameworks. This research intends to explain the significant effects of transparency in dApp blockchain technology. In future, this blockchain technology is going to be useful and incorporated in the field of agriculture. This makes the system a more secure methodology in making the system independent along with the integration advanced AI tools for more security. © 2024 IEEE.

关键词： Smart contract

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Deterministic Algorithm for Cloud Outsourced data

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SN computer science 2022年第3期3卷 223页

作者： Tajammul, Mohd Parveen, Rafat Asif, Sumaira School of Computer Science and Information Technology Jain Deemed To Be University Bangalore India Department of Computer Science Jamia Hamdard University New Delhi India

Cloud storage suffers from security. It is more prone to security breaches due to multitenant architecture and data remanence. Cloud storage is robust as well as promising platform from economic point of view as no extra cost for purchasing hardware is required. Security breaches hamper the market of cloud. Instead of attracting the mind of customers, it creates a picture of dilemma in their minds whether to cloudify or not. Many algorithms have already been designed and implemented to make cloud storage robust and secure place to outsource data but the attacks of twenty-first century uncover that they are inadequate to provide all round security to it. This research paper proposed an algorithm which determines whether the data being uploaded on cloud are sensitive or normal data. On testing data sensitivity, algorithm decides whether to upload it on public cloud or on private cloud. Moreover, if data are found to be sensitive, it is bifurcated and uploaded on private cloud after encryption with two different algorithms, else it is uploaded on public cloud. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud security Cloud storage data bifurcation Deterministic algorithm Sensitive data

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Deep Explainable Ensemble for Medical Image Analysis

Deep Explainable Ensemble for Medical Image Analysis

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Syed, Hassaan Haider Aneja, Nagender Malik, Owais Ahmed Minhaj, Mohammed Abdul Rahman School of Digital Science Universiti Brunei Darussalam Bandar Seri Begawan Brunei Purdue University Department of Computer Science West LafayetteIN United States Hospital Ripas Department of Radiology Kianggeh Brunei

ISBN: (纸本)9798350350821

Traditionally, medical images have been analyzed manually. However, this manual analysis can be subjective, depending on the judgment of radiologists and pathologists, which can result in inconsistency and inefficiency. computer vision and deep learning models have revolutionized the study and diagnosis of medical images such as X-rays, CT scans, and MRIs. Despite their effectiveness, these models often act as black boxes and are difficult to interpret. In this study, we propose an ensemble of two popular deep learning models, ResNet101 and EfficientNetB7, which not only provide higher classification accuracy for CT scans but also offer superior interpretability. Our ensemble model outperforms the original models in COVID-19 CT scan classification by more than 2%. We compared the performance of three eXplainable AI (XAI) visualization models including GradCAM, Guided-GradCAM, and LIME, against the ground truth data labeled by radiologists. The proposed ensemble method provides better interpretability for the CT scan images compared to the individual models. © 2024 IEEE.

关键词： Medical imaging

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Tag Ownership Transfer Protocol Based on Parity Check Patching Operation

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International Journal of Network Security 2024年第1期26卷 84-91页

作者： Li, Zhen-Hui School of Data Science and Computer Science Guangdong Peizheng College Guangzhou510830 China

In the RFID system, the owner of the electronic label may change during its life. In order to ensure the security of the private information stored in the label by other owners when the owner of the electronic label changes, this paper proposes an ultra-lightweight ownership transfer protocol to ensure the security of users’ private information. The proposed protocol is based on parity check patching operation to encrypt the plaintext and then carry out data interaction. Parity check patching operation can be realized by bitwise operation. Combined with the unique hamming weight of encryption parameters, different ways of checking and patching can be carried out according to the different size relations. In the case of not adding new parameters and reducing the storage space, it can also increase the difficulty of cracking. One end of the label introduces a parameter of the attribution status bit, which marks the owning status of the current label. Multiple protocols are analyzed based on GNY logical formal analysis and calculation amount, starting from various specific attack types. The proposed protocol has highsecurity performance and is superior to other comparison protocols in the calculation amount. © (2023) All Rights Reserved.

关键词： Internet of things

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An Ensemble Learning Approach to Improve Credit Scoring Accuracy for Imbalanced data

An Ensemble Learning Approach to Improve Credit Scoring Accu...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Ranpara, Ripal D. Patel, Pratik S. Atmiya University Department of Computer Science & Information Technology Rajkot India Science University School of Cyber Security & Digital Forensic Gandhinagar India

ISBN: (纸本)9798350315455

The assessment of loan risks, known as credit scoring, is a critical procedure within the financial industry. However, this process often faces difficulties as a result of uneven data distributions. Imbalances are evident when one class has a much greater representation than the other, resulting in standard machine learning techniques displaying a bias towards the more prevalent class. The present work presents a new ensemble learning approach that aims to enhance the accuracy of credit scoring in situations when there are imbalanced class distributions. The proposed methodology incorporates a combination of several fundamental classifiers that have been trained on separate subsets of the initial dataset, resulting in a cohesive ensemble model. A series of experiments were done on a credit scoring dataset with unbalanced classes, exploring several ensemble methodologies like as bagging, boosting, and stacking. The results indicate that the ensemble strategy proposed in this study outperforms traditional techniques, exhibiting greater accuracy and F1 score. Furthermore, we conduct an analysis on the effects of various ensemble tactics on performance, offering significant insights into the process of ensemble learning for credit scoring data that is uneven. The technique proposed in this study offers a viable alternative to address the problem of imbalanced credit scoring data, hence improving the accuracy and fairness of credit scoring models in the financial industry. © 2023 IEEE.

关键词： AdaBoost Area under the curve (AUC) Bagging Boosting Credit scoring Ensemble learning Gradient boosting Machine learning Random forest Receiver operating characteristic (ROC) XGBoost

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Faster and Stronger Lossless Compression with Optimized Autoregressive Framework 23

Faster and Stronger Lossless Compression with Optimized Auto...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Yu Mao Jingzong Li Yufei Cui Jason Chun Xue Department of Computer Science City University of Hong Kong School of Computer Science McGill University

ISBN: (纸本)9798350323481

Neural AutoRegressive (AR) framework has been applied in general-purpose lossless compression recently to improve compression performance. However, this paper found that directly applying the original AR framework causes the duplicated processing problem and the in-batch distribution variation problem, which leads to deteriorated compression performance. The key to address the duplicated processing problem is to disentangle the processing of the history symbol set at the input side. Two new types of neural blocks are first proposed. An individual-block performs separate feature extraction on each history symbol while a mix-block models the correlation between extracted features and estimates the probability. A progressive AR-based compression framework (PAC) is then proposed, which only requires one history symbol from the host at a time rather than the whole history symbol set. In addition, we introduced a trainable matrix multiplication to model the ordered importance, replacing previous hardware-unfriendly Gumble-Softmax sampling. The in-batch distribution variation problem is caused by AR-based compression's structured batch construction. Based on this observation, a batch-location-aware individual block is proposed to capture the heterogeneous in-batch distributions precisely, improving the performance without efficiency losses. Experimental results show the proposed framework can achieve an average of 130% speed improvement with an average of 3% compression ratio gain across data domains compared to the state-of-the-art.

关键词： auto-regressive

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BI-Enabled Web Visualization of In-Place Inclinometer data for Geotechnical Applications 23rd

BI-Enabled Web Visualization of In-Place Inclinometer Data ...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Palma, João Marques, Nuno Santos, Ricardo NOVA LINCS NOVA School of Science and Technology Department of Computer Science Quinta da Torre - Campus FCT/UNL Caparica2829-516 Portugal Avenida do Brasil 101 Lisbon Portugal

ISBN: (纸本)9783031735028

IoT integration for continuous monitoring is essential for effective data interpretation, advanced visualization, and timely decision-making in Business Intelligence (BI). Current in-place inclinometer (IPI) systems suffer from outdated visualization tools and limited real-time capabilities. Our solution integrates a low-cost, wireless IPI prototype with a web-based application, providing efficient data preprocessing, intuitive visualization, and real-time monitoring to enhance decision-making and risk management. IPI involve the installation of multiple sensors at discrete depths along a tube casing and are useful when regular monitoring of ground movement is essential or when site accessibility is limited. This approach saves valuable time and labor that would otherwise be spent on manual measurements using the traditional inclinometer probe. Additionally, it enables the seamless collection of regular data through the utilization of automated data acquisition equipment, providing valuable information on the ground deformation vector and rate of movement. Our experiments present visualisations that illustrate the system’s effectiveness in diverse scenarios, highlighting its ability to accurately preprocess and visualize data, facilitate interactive analysis, and provide immediate insights into structural stability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk management

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Task-Oriented Edge-Assisted Cooperative data Compression, Communications and Computing for UGV-Enhanced Warehouse Logistics

Task-Oriented Edge-Assisted Cooperative Data Compression, Co...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jiaming Yang Zhen Meng Xiangmin Xu Kan Chen Emma Liying Li Philip G. Zhao School of Computer Science University of Glasgow UK Department of Computer Science University of Manchester UK

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper explores the growing need for task-oriented communications in warehouse logistics, where traditional communication Key Performance Indicators (KPIs)-such as latency, reliability, and throughput-often do not fully meet task requirements. As the complexity of data flow management in large-scale device networks increases, there is also a pressing need for innovative cross-system designs that balance data compression, communication, and computation. To address these chal-lenges, we propose a task-oriented, edge-assisted framework for cooperative data compression, communication, and computing in Unmanned Ground Vehicle (UGV)-enhanced warehouse logistics. In this framework, two UGVs collaborate to transport cargo, with control functions-navigation for the front UGV and following/conveyance for the rear UGV-offloaded to the edge server to accommodate their limited on-board computing resources. We develop a Deep Reinforcement Learning (DRL)-based two-stage point cloud data compression algorithm that dynamically and collaboratively adjusts compression ratios according to task requirements, significantly reducing communication overhead. System-level simulations of our UGV logistics prototype demon-strate the framework's effectiveness and its potential for swift real-world implementation.

关键词： Point cloud compression Heuristic algorithms Warehousing Prototypes Pressing Land vehicles Servers Reliability Vehicle dynamics Logistics

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Enhancing Bird Species Classification: A Weighted Average Transfer Learning Ensemble Approach 2

Enhancing Bird Species Classification: A Weighted Average Tr...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Murthy Polisetty, Venkata Ramana Chokkalingam, S.P. Chennai601103 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai601103 India

ISBN: (纸本)9798350327533

With the escalating threat to avian life due to environmental degradation, the need for an automatic bird image recognition system has become paramount. The diversity among the estimated 11,000 bird species worldwide presents a challenge, demanding advanced technological solutions for their conservation. The proposal utilizes a custom dataset comprising 525 species to formulate a distinctive ensemble deep-learning model for categorizing bird species. The weighted average ensemble (WAE) technique, specifically combining EfficientNetB5 and VGG19, selected from a pool of five deep learning models, including MobileNetV2, ResNet152, and InceptionV3, attains an impressive 97.18 % accuracy. Using data augmentation approaches, the training data becomes more diverse, strengthening the model. The evaluation metrics extend beyond accuracy, including F1 Scores, comprehensively assessing the model's output quality. © 2024 IEEE.

关键词： Bird Species Classification computer Vision data Augmentation Ensemble Prediction Image Classification Transfer Learning

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Comparing Time Series Assist Vector Machines and Recurrent Neural Networks for Hyper Spectral Image Popularity 5th

Comparing Time Series Assist Vector Machines and Recurrent N...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Goswami, Shubhashish Pasha, Afroz Mishra, Awakash Krishnamoorthy, Ramkumar Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper examines the overall performance of two system mastering models, Time series help Vector Machines (SVMs) and Recurrent Neural Networks (RNNs), on classification of Hyper Spectral photographs (HSIs). The fashions are in comparison based totally on their recognition accuracy which includes the time requirements to build fashions. The paper additionally evaluates two varieties of preprocessing techniques: minimum Redundancy maximum Relevancy (MRMR) and precept aspect evaluation (PCA). The effects of the experiments highlight that SVMs and RNNs perform considerably distinct for HSI class and that counting on one version simplest may lead to exclusive type performance. Moreover, the experiments show that with increasing complexity, the quantity of time required to assemble the RNNs will increase significantly. The comparisons in the paper allow further exploration in the discipline and help the choice of the high-quality model with the aid of capacity practitioners and developers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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