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Securing Medical Records Using Private Blockchain Technology 5

Securing Medical Records Using Private Blockchain Technology

引用

5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Alothman, Basil Alenezi, Mariam Alhamdan, Danah Almutairat, Mariam Almousa, Zahraa Joumaa, Chibli Khan, Murad Kuwait College of Science and Technology Dept. of Computer Science and Engineering Kuwait-City Kuwait

ISBN: (纸本)9798350339239

The term 'electronic medical records' (EMRs) refers to information that is highly confidential and saved electronically. This information is associated with the diagnosis and treatment of a patient, and it must be routinely distributed among the concerned people such as doctors and nurses. The disclosure of private medical information or its alteration during an operation makes it extremely difficult for participants to exchange their medical files with one another. The most effective strategy for overcoming these obstacles is to implement an electronic medical records system that is based on blockchain technology. Within the scope of this study, we have addressed how the technology of blockchain might contribute to improved healthcare data management. Through the application of blockchain technology, the purpose of this research is to establish a connection between the health ministries and departments and both public and private hospitals in order to simplify the process of gaining access to one's medical records and transferring those records while maintaining patient confidentiality and improving safety. © 2023 IEEE.

关键词： Information management

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Association Rules Mining for a Specific Time Period in a Day in Large Transactional database 2

Association Rules Mining for a Specific Time Period in a Day...

引用

2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Islam, Md. Rabiul Rahman, Mst. Sahela Nayeem, Md. Julker Pundra University of Science and Technology Dept. of Computer Science and Engineering Bogura5800 Bangladesh

ISBN: (纸本)9798350358667

Market basket analysis is a trendy topic in the field of data mining. Analyzing the daily transactions, several types of associations were noticed in the transacted item sets. The transactional database used in retail marketing has a large number of itemsets that are frequently purchased exclusively during specific hours of the day and are not usually considered to be frequently purchased. In some instances, association rule mining at a lower data level with uniform support doesn't reveal any notable patterns, yet it may be suppressing crucial data. For instance, the morning session sees the biggest demand for items like bread and cake, while the afternoon and evening sessions find higher demand for items like drinks and noodles. In the transaction at a supermarket, this study discovered the frequent item and the link among the frequent item sets. In this study, we used the tree-based FP-growth idea to locate the pertinent rules in the transaction based on the time of day, specifically the morning, afternoon, and evening. Our empirical analysis demonstrates that the suggested approach is computationally effective in a different time period of the day, enabling the Bangladeshi retailer Super Shop to make the best decisions and boost sales. © 2023 IEEE.

关键词： Association rules

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Graph Based Keywords Extraction from Crime data 16

Graph Based Keywords Extraction from Crime Data

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-dept. police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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Enhanced Brain Tumor Segmentation: A Study on the Efficacy of Multi-Model Ensembling Utilizing Diverse Backbones 26

Enhanced Brain Tumor Segmentation: A Study on the Efficacy o...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md. Hedayatul Hossain, Md. Rakib Akter, Farzana Srizon, Azmain Yakin Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Islam, Md. Minhazul Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Gliomas account for approximately 80% of all malignant brain tumors and are associated with the most unfavourable survival rates amongst all brain tumor types. Segmenting these tumors is integral to evaluating their dimensions and morphology, formulating tailored treatment plans, and estimating patients' life expectancy. Various approaches for brain tumor segmentation have been proposed, yet the model described herein stands out, offering promising results in this vital task. A comprehensive examination of three eminent convolutional neural network (CNN) models, namely Resnext50, Resnet50, and InceptionV3, was conducted, each characterised by its own unique structural framework. The enhancement of the segmentation approach's effectiveness was a focal point, leading to the exploration of ensemble modeling. This approach leverages the collective strengths of the aforementioned models, facilitated by softmax averaging. This collaborative initiative culminated in the creation of the Ensemble model, which outshoned its individual counterparts in performance metrics. Furthermore, the Ensemble model is poised to instigate significant advancements in brain tumor diagnostic procedures. The ensemble methodology is instrumental in refining the precision and automation of brain tumor diagnoses. Deep learning, as evidenced by the research, is a transformative element in medical image analysis, elevating the accuracy and efficiency of brain tumor segmentation. Consequently, this refined approach is set to become an invaluable asset for medical professionals in enhancing the accuracy of diagnoses and the efficacy of treatments for patients afflicted with brain tumors. © 2023 IEEE.

关键词： Backbone Architectures Brain Tumor Segmentation Deep Learning Ensemble Learning Evaluation Metrics Medical Image Analysis MRI Images U-Net

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Predictive Analytics in Agriculture: Unraveling the Determinants of Crop Yield with Machine Learning 26

Predictive Analytics in Agriculture: Unraveling the Determin...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Sakib, Shadman Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Basak, Anindya Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

In the context of Bangladesh, the precision in forecasting crop yields has been enhanced significantly through the integrative approach of amalgamating historical data, environmental variables, and cutting-edge technology. This integrative methodology underscores the fortification of the nation's agricultural domain, integral to both its economic fabric and the populace's sustenance. This study presents an integrative approach leveraging historical data, environmental variables, and advanced technology to enhance crop yield forecasting. Traditional agricultural methods, while valuable, often lack comprehensive understanding of climatic factors, limiting agricultural refinement. Employing an Advanced computer-Aided Decision Support System, this research meticulously analyzes extensive datasets to uncover intricate correlations between historical yields and environmental nuances. In this study, a novel interpretative machine learning model is formulated, emphasizing data pre-processing and optimal feature transformation protocols tailored to diverse model archetypes. The study delves into the influence of distinct variables on predictive acumen and the impact of individual observational values on forecast outcomes. The study's contributions include a transparent machine learning model elucidating key variables affecting crop yield predictions, paving the way for informed agricultural practices. Future endeavors could focus on leveraging real-time data and expanding datasets to further refine predictive models in this crucial domain. © 2023 IEEE.

关键词： Crop Yield Prediction Feature Importance Machine Learning Parameter Optimization

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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation 2

Blockchain Based Secure and Decentralized Smart Licensing of...

引用

2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Haque, Momenul Paul, Subrata Kumer Islam, Kamrul Della, Mursheda Nusrat Paul, Rakhi Rani Fahim, Sultan Bangladesh Army University of Engineering and Technology Dept. of Computer Science and Engineering 6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Bangladesh Army University of Engineering and Technology Dept. of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350358667

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city. © 2023 IEEE.

关键词： Smart contract

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A Time-varying Velocity Control Strategy for Balancing Exploration and Exploitation Behaviour of a Binary PSO

A Time-varying Velocity Control Strategy for Balancing Explo...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Siddique, Md. Abu Bakkar Islam, Md. Jakirul DUET Gazipur Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

Combinatorial optimization problems (COPs) are NP-hard, and their solution space is discrete, which poses a significant challenge for optimization algorithms. Binary Particle Swarm Optimization (BPSO) is wel-known optimization technique that has been widely applied in various type of COPs. However, one of the challenges in BPSO is to balance exploration and exploitation to locate the optimal solution. This paper presents a Time-varying Velocity Control (TVC) strategy to balance exploration and exploitation in a BPSO. The TVC strategy utilizes a time-varying control parameter to adjust the velocity of the particles, allowing for more exploration in the early phases of the optimization process and more exploitation as the search progresses. The proposed strategy is evaluated on a set of benchmark combinatorial optimization problems, and the results demonstrate that it outperforms other commonly used BPSO strategies in terms of both efficiency and solution quality. Furthermore, the TVC strategy is shown to be robust to changes in the problem landscape, making it a promising approach that can be used for solving a wide range of combinatorial optimization problems. © 2023 IEEE.

关键词： Combinatorial optimization

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Software Code De-Compilation Techniques and Approaches: A Comparative Study 25

Software Code De-Compilation Techniques and Approaches: A Co...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Fawareh, Hamed Al-Smadi, Yazan Hassan, Mohammad Fawareh, Faid AlNoor Elrashidi, Ali Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. of Computer Science Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language. © 2024 IEEE.

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Multi Layered Defense Architecture for DDoS Protection in Private Cloud Environments 2

Multi Layered Defense Architecture for DDoS Protection in Pr...

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

作者： Singh, Shashwat Sharma, Anubha Soni, Shani Prakash, Ayushi Ajay Kumar Garg Engineering College Dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350368109

Private cloud solutions have increasingly been adopted because of the demand from organizations for enhanced data security, control over regulatory compliance, and IT infrastructure has been rising. Although public cloud services offer built-in security solutions, private clouds must possess a custom built architectures to address critical challenges, including Distributed Denial of Service (DDoS) attacks, among *** are often deployed behind private cloud environments without the benefits of prebuilt SaaS based DDoS protection. In this exposition, we present an architecture for multilayer protection against DDoS attacks in private cloud environments, which includes the following key components: Global Load Balancer, Traffic Scrubbing Center, Signature based IDPS, and Command and Control Center. Further, we also present a custom Command and Control Center, which utilizes CNNs for runtime handling of network traffic flow, based on real-time monitoring and activity patterns, to mitigate the menace of DDoS. Experimental results show that the architecture is efficient in filtering volumetric and application layer DDoS attacks, ensuring a malicious packet drop rate greater than 95%, while maintaining latency below 150 ms. © 2024 IEEE.

关键词： Cloud security

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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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