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2020 ACM International Conference on Intelligent Computing and its Emerging Applications, ICEA 2020

作者： Kwon, Soonhong Choi, Yangseo Lee, Jong-Hyouk Dept. of Computer and Information Security and Covergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of Electronics and Telecommunication Research Institute Daejeon Korea Republic of

ISBN: (纸本)9781450383042

Based on the 4th Industrial Revolution, numerous ICT technologies are developing, and for this reason, IoT devices are formed around us. Accordingly, hackers are inflicting financial and physical damage to our lives by using software vulnerabilities in IoT devices around us based on intelligent hacking technology. Automated security vulnerability response systems are required to respond to attacks through continuously occurring software vulnerabilities. In this paper, we analyze the hybrid fuzzing system that complements the technical limitations of the existing fuzzing technology as the base technology for an automated security vulnerability response system. In addition, we propose a hybrid fuzzing system based on an automatic seed generation mechanism for coverage efficiency in order to find vulnerabilities inherent in software quickly and efficiently. © 2020 ACM.

关键词： Efficiency

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Evolutionary Interest Representation network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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IEEE International Conference on Web Services (ICWS)

作者： Renjie Zhou Zhenming Jin Jian Wan Jilin Zhang Xingyu Guo Qiang Liu Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Dept. of Computer and Information Technology Zhejiang Police College Hangzhou China

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.

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Queuing Theory Model of Expected Waiting Time for Fast Diagnosis nCovid-19: A Case Study 3

Queuing Theory Model of Expected Waiting Time for Fast Diagn...

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3rd International Conference on Advanced Science and engineering, ICOASE 2020

作者： Majedkan, Naji A. Idrees, Barzan A. Ahmed, Omar M. Haji, Lailan M. Dino, Hivi Ismat Technical College of Informatics - Akre Duhok Polytechnic University Dept. of Network and Information Security Duhok Iraq College of Physical Education and Sport Sciences University of Duhok Duhok Iraq Zakho Technical Iust Duhok Polytechnic University Zakho Iraq University of Zakho Computer Science Dept. Zakho Iraq University of Zakho Dept. of Computer Science Duhok Iraq

ISBN: (纸本)9781665415798

This Queuing theory analysis has been used in hospitals and other healthcare settings;its use in this sector is not widespread. Consequently, the queue waiting line is an effective scientific tool in the performance of waiting lines analysis. The main parameters used to measure the performance of the systems are the length of the queue line, utilization of the server, and the delays for arrivals. This study aims to avoid others to expose from epidemics such as (nCovid-19), because of becoming a big global problem today in the world. Also, to know the length for the expected and actual waiting times to diagnosis the arrivals to Duhok city. Collection and execution are done within one week with one activity healthcare team's (HCT) in the main entry Duhok city, port. Data was collected utilize individual conceptions and records from Saturday through to Thursday, which they are the most critical time setting. The main interest in this study was calculating the average waiting time spent after the process to improve arrivals satisfaction using the queuing theory model. The results of this analysis indicate for each citizen checked by the healthcare team may be waiting 32.44 minutes in a queue. Also, was estimate to provide the results of system capabilities with characterization related to the expected waiting time. Finally, Medical staff at the city outlets can estimate;how many arrivals will be waiting in the line and the number of clients that will walk away each day. © 2020 IEEE.

关键词： Diagnosis

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Twenty Years of Personality Computing: Threats, Challenges and Future Directions

arXiv

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arXiv 2025年

作者： Celli, Fabio Kartelj, Aleksandar Ðorðević, Miljan Suhartono, Derwin Filipović, Vladimir Milutinović, Veljko Spathoulas, Georgios Vinciarelli, Alessandro Kosinski, Michal Lepri, Bruno Maggioli SpA Italy Faculty of Mathematics University of Belgrade Serbia School of Electrical Engineering University of Belgrade Serbia School of Computer Science Bina Nusantara University Indonesia Dept. of Information Security Norwegian University of Science and Technology Norway School of Computing Science University of Glasgow United Kingdom Graduate School of Business Stanford University United States Center for Augmented Intelligence Fondazione Bruno Kessler Italy

Personality Computing is a field at the intersection of Personality Psychology and computer Science. Started in 2005, research in the field utilizes computational methods to understand and predict human personality traits. The expansion of the field has been very rapid and, by analyzing digital footprints (text, images, social media, etc.), it helped to develop systems that recognize and even replicate human personality. While offering promising applications in talent recruiting, marketing and healthcare, the ethical implications of Personality Computing are significant. Concerns include data privacy, algorithmic bias, and the potential for manipulation by personality-aware Artificial Intelligence. This paper provides an overview of the field, explores key methodologies, discusses the challenges and threats, and outlines potential future directions for responsible development and deployment of Personality Computing technologies. © 2025, CC BY-NC-ND.

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Ransomware: A framework for security challenges in internet of things 2

Ransomware: A framework for security challenges in internet ...

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2nd International Conference on computer and information Sciences, ICCIS 2020

作者： Saeed, Soobia Jhanjhi, N.Z. Naqvi, Mehmood Humayun, Mamoona Ahmed, Shakeel Universiti Teknologi Malaysia Dept. of Software Engineering Malaysia Taylor's University Selangor Malaysia Faculty of Engineering Technology Mohawk College Canada Jouf University Dept. of Information Systems Al-Jouf Saudi Arabia King Faisal University Dept. of Computer Science Al-Hofuf Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

With the increasing volume of smartphones, computers, and sensors in the Internet of Things (IoT) model, enhancing security and preventing ransom attacks have become a major concern. Traditional security mechanisms are no longer applicable due to the involvement of devices with limited resources, which require more computing power and resources. Ransomware is comparatively a new and cruel malware in cyberspace with higher rates of attacks around the world. Ransomware could encrypt entire data to make users unable to access their files and important information. In some cases, the system has been hostage completely by the hackers, and the user may receive a demand for ransom money using different resources o access of his/her own data/system. One of the problems associated with the Internet of Things is how to keep your smartphones secure and keep your data safe as most of the antivirus solutions are not useful in this case. This research concludes the impact of ransomware on the IoT, malware processes, and work on detecting and monitoring smartphone infections. The paper also discusses ransomware awareness to end-user with strategy to defeat it. © 2020 IEEE.

关键词： Smartphones

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LSTM networks for Improved Environmental Monitoring and Biodiversity Conservation

LSTM Networks for Improved Environmental Monitoring and Biod...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Dara Raju Omaia Al-Omari Ganesh Kumar Anbazhagan Kuttimarks Muthu Selladurai Malik Bader Alazzam M. Karthik Dept. of CSE Vignana Bharathi Institute and Technology Ghatkesar India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Department of Microbiology Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Shivajirao S. Jondhle College of Engineering and Technology Maharastra India Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Understanding and managing the effects of climate change and loss of biodiversity are crucial. However, traditional approaches are largely ineffective in dealing with these challenges because environmental data is complex and uncertain. Environmental monitoring thus comes into play. To this end, this paper presents an investigation into the LSTM network, a type of recurrent neural network that was developed to capture long-range patterns in sequential data. With the ability of LSTM networks to process historical data and understand temporal relationships, the prediction of climate variables like temperature, precipitation, and extreme weather occurrences is now much more advanced. As a result, this has led to the development of more accurate climate models, which are essential for creating mitigation and adaptation plans for climate change. Analyzing time-series data, LSTM networks can mimic species population dynamics and habitat changes in biodiversity conservation. This is helpful in predicting future population trends and identifying critical times of vulnerability. This ability allows for timely and effective conservation. Huge environmental datasets are collected and preprocessed, the models' excellent performance in environmental monitoring tasks is further proved by their 97.6% accuracy. A network of LSTM is established with the aim of improving the accuracy of environment-science forecasting and enhancing associated decision-making processes, which eventually lead to more resilient and sustainable resources management.

关键词： Climate change Temperature distribution Accuracy Recurrent neural networks Biological system modeling Environmental monitoring Biodiversity Resource management Environmental management Long short term memory Climate change

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Big Data against security Threats: The SPEAR Intrusion Detection System 3

Big Data against Security Threats: The SPEAR Intrusion Detec...

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3rd World Symposium on Communication engineering, WSCE 2020

作者： Pliatsios, Dimitrios Sarigiannidis, Panagiotis Psannis, Konstantinos Goudos, Sotirios K. Vitsas, Vasileios Moscholios, Ioannis University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani50100 Greece University of Macedonia Dept. of Applied Informatics Thessalonikii54636 Greece Aristotle University of Thessaloniki School of Physics Thessaloniki54124 Greece International Hellenic University Dept. of Information and Electronic Systems Engineering Thessaloniki57400 Greece University of Peloponnese Dept. of Informatics and Telecommunications Tripolis22100 Greece

ISBN: (纸本)9781728185644

The environmental concerns, the limited availability of conventional energy sources, the integration of alternative energy sources and the increasing number of power-demanding appliances change the way electricity is generated and distributed. Smart Grid (SG) is an appealing concept, which was developed in response to the emerging issues of electricity generation and distribution. By leveraging the latest advancements of information and Communication Technologies (ICT), it offers significant benefits to energy providers, retailers and consumers. Nevertheless, SG is vulnerable to cyber attacks, that could cause critical economic and ecological consequences. Traditional Intrusion Detection systems (IDSs) are becoming less efficient in detecting and mitigating cyberattacks, due to their limited capabilities of analyzing the exponentially increasing volume of network traffic. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) platform, which features a Big Data enabled IDS that timely detects and identifies cyber attacks against SG components. In order to validate the efficiency of the SPEAR platform regarding the protection of critical infrastructure, we installed the platform in a small wind power plant. © 2020 IEEE.

关键词： Smart power grids

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Modeling and Analysis the Impact of Wastewater Sludge Co-Digestion with Thermo-Alkaline Pretreated Wheat Straw on Biogas Production and Digestate Characteristics at Al-Jouf Region, Saudi Arabia

SSRN

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SSRN 2023年

作者： Alrowais, Raid Said, Noha Mahmoud-Aly, Mohamed Helmi, Ahmed M. Nasef, Basheer M. Mohareb, Mohamed Abdel daiem, Mahmoud M. Department of Civil Engineering College of Engineering Jouf University Sakakah72388 Saudi Arabia Environmental Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt Plant Physiology Division Department of Agricultural Botany Faculty of Agriculture Cairo University Giza12613 Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Buraydah51418 Saudi Arabia Computer and Systems Engineering Dept. Faculty of Engineering Zagazig University Zagazig44519 Egypt Civil Engineering Department College of Engineering Shaqra University Al-Duwadmi Ar Riyadh11911 Saudi Arabia Alwatania Contracting Company Operation and Maintenance of Sewage Stations and Networks Sakakah Saudi Arabia

Anaerobic digestion is one of the best options for producing valuable end products (biogas and bio-fertilizer). In this study, the impact of co-digestion of waste activated sludge with pretreated wheat straw on biogas production and digestate characteristics was investigated. Thermo-alkaline pretreatment of straw has been conducted using NaOH, KOH, and Na2CO3 at different durations (1, 3, and 5h). A feedforward neural network (FFNN) and Logistic models, and SPSS analysis were applied on the experimental data to predict biogas and investigate the significance and the relationship among the variables. NaOH pretreatment for 5h showed the best treatment conditions;the biogas yield reached to 6.59 folds compared to without treatment, and removal ratio of total solids, total volatile solids, chemical oxygen demand, and microbial count reached 63.52 %, 74.60 %, 78.15 %, and 82.22 %, respectively. Methane content reached 67.50 %, indicating a high quality of the biogas. The thermo-alkaline pretreatment induced significant effects on biogas production and digestate characteristics that can be used as bio-fertilizers. Experimental data were successfully modeled for predicting biogas production using the applied models;R2 reached 0.9846 and 0.999 for Logistic and FFNN models, respectively. © 2023, The Authors. All rights reserved.

关键词： Biogas

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Improving performance of federated learning based medical image analysis in non-IID settings using image augmentation

arXiv

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arXiv 2021年

作者： Cetinkaya, Alper Emin Akin, Murat Sagiroglu, Seref Information Security Program Gazi University Ankara Turkey Gazi AI Center of Gazi University Basarsoft Information Systems Inc Ankara Turkey Computer Engineering Dept. Gazi AI Center Gazi University Ankara Turkey

Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating multiple edge devices or organizations to contribute a training of a global model using a number of local data without having them. Non-IID data of FL caused from its distributed nature presents a significant performance degradation and stabilization skews. This paper introduces a novel method dynamically balancing the data distributions of clients by augmenting images to address the non-IID data problem of FL. The introduced method remarkably stabilizes the model training and improves the model’s test accuracy from 83.22% to 89.43% for multi-chest diseases detection of chest X-ray images in highly non-IID FL setting. The results of IID, non-IID and non-IID with proposed method federated trainings demonstrated that the proposed method might help to encourage organizations or researchers in developing better systems to get values from data with respect to data privacy not only for healthcare but also other fields. Copyright © 2021, The Authors. All rights reserved.

关键词： Data privacy

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In-dept. review of integration of AI in cloud computing

In-depth review of integration of AI in cloud computing

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Nachaat Mohamed L. Sridhara Rao Manju Sharma SureshBabuRajasekaranl BadriaSulaimanAlfurhood Surendra Kumar Shukla Rabdan Academy UAE Homeland Security Universiti Sains Malaysia Abu Dhabi Dept. of Information Technology J.B. Institute of Engineering and Technology Hyderabad Lecturer Jazan University Jazan Saudi Arabia Group Product Manager NVIDIA Corporation US Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

The successful distribution of computer services, including software, storage needs, analytics, intelligence, and many others, is reflected in cloud computing. Additionally, it provides quicker innovation while simultaneously providing flexible *** learning (DL), a subset of artificial intelligence (AI) and machine learning, is widely seen as a key technology of the Fourth Industrial Revolution (4IR or Industry 4.0). The artificial neural network (ANN)-based technique known as deep learning (DL) has gained popularity in the computing world due to its capacity for learning from data. frequently used in a variety of application fields, including cybersecurity, healthcare, visual recognition, and many more. However, because of the dynamic nature and differences in real-world environments, creating an acceptable DL model is a difficult process. issues and information Additionally, because of a lack of fundamental knowledge, DL techniques become black-box devices that hinder standard level progress.A cloud computing system in an organization offers high data security at a cheap maintenance cost. Business cooperation has increased significantly as a result of cloud technology. Use of AI has enhanced each of those procedures. The use of artificial intelligence in certain industries has a considerable impact on how successful cloud services techniques are, resulting in a consequence, the two innovations' cumulative effect boosts the prosperity of such businesses. The use of smart - device and computer vision models improve the effectiveness of public clouds. It has also demonstrated implementing AI into public cloud strategies may benefit enterprises in a variety of *** essay includes a thorough introduction and an insightful literature review. Three objectives are outlined in a methodological approach that has also been given.

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