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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Al-Amain Fatema Tuj Janin Faizul Ahmed Robin Shifat Ahmed Khandaker Mohammad Mohi Uddin Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030.

关键词： Support vector machines Machine learning algorithms Accuracy Liver diseases Predictive models Prediction algorithms Classification algorithms Viruses (medical) Medical diagnostic imaging Principal component analysis

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Reinforcement Learning for a Raspberry Pi Smart Car for Lane Detection Using OpenCV

Reinforcement Learning for a Raspberry Pi Smart Car for Lane...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Minh Nguyen Tauheed Khan Mohd Dept. of Math and Computer Science Augustana College Rock Island Illinois United States Dept of Math and Computer Science Augustana College Rock Island Illinois

The paper aims to represent an autonomous car prototype using the Raspberry Pi as a processing chip. The pi-camera module provides necessary data from the real world to the car, which then passes the data on to the raspberry-pi. The car can follow a given path safely and intelligently, thus avoiding the risk of human errors by responding to the real-time curve.

关键词： Performance evaluation Deep learning Lane detection Prototypes Reinforcement learning Real-time systems Nanoscale devices

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Securing Internet of Vehicles Protocols using ASCON and GIFT-COFB

Securing Internet of Vehicles Protocols using ASCON and GIFT...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Wissal BenMassaoud Darshan M Rutvij H. Jhaveri Gautam Srivastava Dept. of Computer Science Lakehead University Thunder Bay ON Canada Dept. of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India Dept. of Math and Computer Science Brandon University Brandon MB Canada

The Internet of Vehicles (IoV) is a network of vehicles that are connected through the Internet using communication protocols. However, this raises concerns about security and privacy. The vulnerability of object networks has been highlighted in recent reports on cybernetics, and attacks on these networks could pose significant dangers to data integrity. To avoid this, security measures such as resistance to attacks, data authenticity, access control, and privacy must be integrated. The limited capabilities of IoT objects in terms of processing power, memory, bandwidth, and energy have led to the creation and redesign of protocols and architectures that meet the specific needs of these networks. One such protocol is the Message Queue Telemetry Transport (MQTT), which uses a Publish/Subscribe methodology and small amounts of bandwidth. To address security concerns, the paper proposes using the lightweight cryptographic techniques ASCON and GIFT-COFB, which could be useful in securing communication and data exchange in IoV sensor networks.

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Is Gamification a Practical Solution for Increasing Repurchase Intention of Online Customers?

Is Gamification a Practical Solution for Increasing Repurcha...

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2023 Congress in computer science, computer Engineering, and applied Computing, CSCE 2023

作者： Safari, Khalil Safari, Zohreh Keshavarzi, Fatemeh Payam Noor University Dept. of Business Management and Information Technology Tehran Iran Elizabeth City State University Dept. Computer Science Math Engineering Technology Elizabeth City United States Qazvin Islamic Azad University Dept.Information Technology Engineering Qazvin Iran

ISBN: (纸本)9798350327595

With the growth of online shopping, organizations should update their portals and websites in response to changing conditions and competitors' actions. To attract and retain online customers, various tools such as reputation building, trust en-hancement, and gamification are utilized and reinforced. This study has investigated 243 users of DigiKala company through paper and electronic questionnaires. Due to the non-normality of the data, non-parametric statistical methods using SPSS and Smart-PLS3.0 software were employed to analyze structural relationships. Additionally, machine learning tools were utilized to optimize the analysis of customer behavior and identify patterns for effective customer retention strategies. The results show that contrary to the research background, the use of gamification on DigiKala's website has not had a positive and significant impact on customers using this company's website and their repurchase intention. Thus, it seems that DigiKala should focus on context and infrastructures, while simultaneously strengthening and revamping gamification dimensions. © 2023 IEEE.

关键词： Gamification intention to use repurchase intention Reputation Trust in vendor

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Neuro-heuristic Pallet Detection for Automated Guided Vehicle Navigation

Neuro-heuristic Pallet Detection for Automated Guided Vehicl...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Silesian University of Technology Faculty of Applied Mathematics Kaszubska 23 Gliwice44-100 Poland Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada

ISBN: (纸本)9781665480451

Automated guided vehicles (AGV) allow for the automation of operations in warehouse environments. From an application point of view, vehicles can use sensors to move and perform a variety of tasks, including moving objects. In this paper, we focus on the analysis of the environment and the preparation of data for vehicle navigation. The proposed solution is based on two paths of action. In the first, the image of the room is processed by heuristics to locate the robot's target - the palette. The found pattern allows one to locate the destination as well as create a mask. The mask can be used to train the U-Net network. When a network is trained, the use of heuristics for pallet location can be omitted. Locating the target allows the image to be processed to obtain an AGV navigation map. The proposed solution based on heuristics and U-Net networks has been described and tested in simulations to indicate the potential of the proposed approach. © 2022 IEEE.

关键词： Navigation

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Vehicle To Vehicle and Vehicle To Ground Communication - Speech Encryption Algorithm

Vehicle To Vehicle and Vehicle To Ground Communication - Spe...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Obaida M. Al-Hazaimeh Ma'moun A. Al-Smadi Dept. of Computer Science Al-Balqa Applied University Irbid Jordan Dept. of Electrical Engineering Al-Balqa Applied University Irbid Jordan

Vehicle to vehicle (V2V) communication has many applications in intelligent transportation system (ITS) like smart grid systems, fleet management and traffic light control. It aims to interconnect several components like road infrastructures, wireless networks, sensors, and vehicles. Therefore, it can be utilized to reduce congestion, accidents and fuel consumption. This paper propose a speech encryption system based on a one-dimensional logistic map for vehicle to vehicle communication (V2V) communication in intelligent transportation systems (ITS) to improve road safety and efficiency, where encryption and security play a major role. This study employs a symmetric key cryptography technique for chaotic ciphering. In this approach, a random key table lookup criterion was paired with a one-dimensional chaotic map in order to encrypt speech samples with a high level of security and acceptable speed. XOR encryption and speech row shuffling are used in the proposed technique. Using the difference between the old and new row locations, a combination of variable rotation and inversion was performed to each shuffled row in order to increase the level of security. As a consequence of the testing, the proposed approach was shown to be both efficient and usable. Combining key table and logistic map illustrates the advantages of both high-level security and large random key space. The encrypted speech produced is suitable for practical usage especially in vehicle to vehicle and vehicle to base transmission.

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Overcoming Imbalanced Datasets and Feature Complexity in Fraud Transaction Detection Through Down-Sampling and Dimension Reduction 17

Overcoming Imbalanced Datasets and Feature Complexity in Fra...

引用

17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Shan, Chan Pei Leng, Too Ai Ahmad, Muneer Malik, Yasir Jaffar, Fehmi Universiti Malaya Dept. of Information Systems Malaysia Digby Stuart College University of Roehampton United Kingdom Bishops University Dept. of Computer Science Canada Université du Québec à Chicoutimi Département d'Informatique et de Mathématique Canada

ISBN: (纸本)9798350381320

Detecting fraudulent transactions presents two primary obstacles, the presence of imbalanced datasets and the sheer volume of feature categories. The scarcity of fraudulent instances within the dataset often results in these cases being erroneously classified as noise, consequently introducing bias towards non-fraudulent cases in the detection outcomes. Simultaneously, the extensive array of feature categories poses a formidable challenge in identifying crucial variables, thereby limiting the efficacy of detection algorithms. This research contributes to the field of fraud transaction detection by proposing a multifaceted approach that addresses the challenges of imbalanced data and feature complexity. Through down-sampling, dimension reduction, model evaluation, and precision enhancement, the study aims to improve the accuracy and efficiency of fraud detection models, with the goal of reducing false alarms and financial losses in real-world scenarios. © 2023 IEEE.

关键词： Principal component analysis

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Modeling Automatic Emergency Braking System Using Fuzzy Timed Petri Nets Based on the 3-Second Rule

Modeling Automatic Emergency Braking System Using Fuzzy Time...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： A. Serji E. B. Mermri M. Blej dept. of Math. and Computer Science Mohammed Premier University Oujda Morocco

Automatic Emergency Braking (AEB) systems play a critical role in enhancing road safety by assisting drivers in avoiding or mitigating collisions. This paper proposes a comprehensive method to model AEB systems using Fuzzy Timed Petri Nets tailored for the 3-second rule. The 3-second rule dictates that a vehicle should maintain a safe distance of at least 3 seconds from the advanced vehicle to minimize the risk of forward collisions. It is important to note that this paper's scope is limited to applying the three-second rule under specific conditions. We focus on scenarios where the weather is clear and ideal for driving, the road conditions are standard without any anomalies, and the vehicles involved are in optimal working conditions without mechanical faults. This narrowed focus allows for more in-dept. analysis and understanding of the AEB system's performance under these parameters. By incorporating FTPN, which combines the benefits of Timed Petri Nets and fuzzy logic, we can effectively capture timing constraints and uncertainties associated with the AEB system. By integrating Artificial Intelligence (AI) technologies by combining the strengths of Petri Nets and fuzzy logic, FTPN allows us to accurately represent and infer real-time systems while effectively capturing the timing constraints and uncertainties inherent in reliable performance in critical scenarios for our system. Our model aimed at determining the decision of emergency Braking based on rules that consider the vehicle's speed, the distance to the vehicle in front, and the safety distance. An Expert System derives several rules from our database and presents them as Fuzzy Timed Production Rules (FTPR). The conversion from FTPR to FTPN is crucial in building this model since we cannot model this system without the correct formulation of the rules. The based model depicts a real-time system that responds to the surroundings and its speed to determine when it may start braking and to what extent it w

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The Use of Encryption to Preserve the Privacy and Security of Electronic Health Records

The Use of Encryption to Preserve the Privacy and Security o...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Naila Samar Naz Dept. of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

Cloud computing is the storage of data over the Internet rather than locally on a user's device, and it is one of the most extensively used applications of computers and related technologies in today's world. Since cloud computing became publicly available, its popularity has risen. End customers may be able to obtain a better application that is more convenient, less expensive, and both effective and efficient because of cloud computing. When storing and managing sensitive data, it is critical to select a storage and management solution that provides security and privacy safeguards. Patients have long been maintained on the cloud, but the data has always been protected by ciphertext encryption. One of the most challenging challenges in a cloud computing environment is the need to protect users' online activities as well as their personal information. The healthcare system was created with the intention of addressing the demands of medical practitioners by providing a solid foundation, simple means to obtain patient information, and efficient ways to use the system. This work describes, with implementation details, an innovative approach for establishing a reliable and effective e-health cloud model employing identity-based encryption. This approach is suggested in this article. The time, power, and energy requirements for encryption and decryption of the suggested strategy were estimated and compared to those of the currently used method. It has been demonstrated that the cryptographic approach presented can reduce the time required for data decoding by up to 50%. Because the decryption process is quicker, the cryptographic procedures may be conducted with less power.

关键词： Cloud computing Privacy Identity-based encryption Public key Medical services Solids Energy efficiency

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Towards Arabic Multimodal Dataset for Sentiment Analysis 3

Towards Arabic Multimodal Dataset for Sentiment Analysis

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3rd International Conference on Intelligent Data science Technologies and Applications, IDSTA 2023

作者： Haouhat, Abdelhamid Bellaouar, Slimane Nehar, Attia Cherroun, Hadda Université Amar Telidji Lab. d'Informatique et de Mathématiques Laghouat Algeria Dept. of Mathematics and Computer Science Algeria Université Ziane Achour Djelfa Algeria Laghouat Algeria

ISBN: (纸本)9798350339253

Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) models has proven their high efficiency for a wide range of Western languages. In contrast, Arabic DL-based multimodal sentiment analysis (MSA) is still in its infantile stage due, mainly, to the lack of standard datasets. In this paper, our investigation is twofold. First, we design a pipeline that helps building our Arabic Multimodal dataset leveraging both state-of-the-art transformers and feature extraction tools within word alignment techniques. Thereafter, we validate our dataset using state-of-the-art transformer-based model dealing with multimodality. Despite the small size of the outcome dataset, experiments show that Arabic multimodality is very promising. © 2023 IEEE.

关键词： Sentiment analysis

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