2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)最新文献

{"title":"Examining Journalistic Practices in Online Newspapers in the Era of Artificial Intelligence","authors":"Marcelle A. Al Jwaniat, D. Tahat, Razan AlMomany, K. Tahat, M. Habes, A. Mansoori, Ihsan Maysari","doi":"10.1109/ICCNS58795.2023.10193525","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193525","url":null,"abstract":"This study aimed to determine the extent to which Jordanian journalists in digital newspapers are aware about artificial intelligence (AI) techniques in journalistic practices and to identify the most important applications, difficulties, and motives for employing AI from the perspectives of 59 journalists in Jordanian digital newspapers. An online questionnaire was used. The results were as follows: the weak reality of employing AI techniques in journalistic practices; few journalists know about the technologies sufficiently; and the most used systems in journalistic practices are photo and video verification techniques, followed by breaking news tracking techniques. The findings revealed that economic factors and the absence of technological infrastructure in press institutions are among the most important factors affecting the adoption of AI techniques in Jordanian digital newspapers. This study had some limitations that should be addressed in future studies.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115590789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flexible Connection Hand-off: A Software-Defined Networks Load Balancing Algorithm","authors":"B. Athamena, Z. Houhamdi","doi":"10.1109/ICCNS58795.2023.10193164","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193164","url":null,"abstract":"This paper proposes a new load-balancing algorithm for Software Defined Networking (SDN) framework. The algorithm emphasizes the Flexible Connection Hand-off (FCH) that considers the maximum load and the difference of loads between the Access Points. The load represents the number of connected stations to a particular Access Point. The proposed algorithm optimizes the load between Access Points by considering the fairness index.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129724862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities and Challenges of OIRS-assisted UAV-based FSO Communication Systems","authors":"Priyanka Singh, Haythem A. Bany Salameh, V. Bohara, A. Srivastava, M. Ayyash","doi":"10.1109/ICCNS58795.2023.10193665","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193665","url":null,"abstract":"Free-space optical (FSO) communication is one of the most cost-effective wireless solutions that can fulfill the requirement of high quality of service (QoS) to the user in the next-generation wireless networks, i.e., beyond fifth-generation (B5G) and sixth-generation (6G). However, one of the major problems associated with the FSO-based communication system is the requirement of line-of-sight (LoS) between the transmitter and the receiver. Integration of optical intelligent reflective surface (OIRS) with FSO-based communication systems can improve the likelihood of establishing LoS links to the receivers. Furthermore, the performance of OIRS with FSO-based communication can be further improved by the utilization of unmanned aerial vehicles (UAVs) thanks to their inherent attractive attributes such as flexibility, mobility, and reconfigurable altitude. UAVs can work as mobile aerial relays and offer enhanced coverage, capacity, and QoS in next-generation wireless networks. In particular, the integration of FSO-based communication systems with an OIRS-assisted UAV could become a promising enabling technology for high-speed, ultra-reliable B5G and 6G communication. In this paper, we provide an overview and analysis of the state-of-the-art OIRS-assisted UAV-based FSO communication systems. In addition, we discuss the key design challenges related to the practical implementation of such communication systems. Furthermore, some potential research directions and open problems pertaining to OIRS-assisted UAV-based FSO communication systems are discussed.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114470314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel Programming for Portfolio Optimization: A Robo-Advisor Prototype using Genetic Algorithms with Recurrent Neural Networks","authors":"Brian Wesley Muganda, Bernard Shibwabo Kasamani","doi":"10.1109/ICCNS58795.2023.10193396","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193396","url":null,"abstract":"This paper presents a robo-advisory prototype that synergizes a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model for asset price forecasting and genetic algorithm for the portfolio optimization problem. The LSTM model was found to be proficient in predicting the prices of Gasoline/Brent Crack Spread, Heating Oil/Brent Crack Spread, and Gas/Crude Oil Energy Spread with an RMSE between 2.25 and 5.62, confirming its capacity for accurate forecasting with minimal computational latency. The genetic algorithm for portfolio allocation demonstrated dynamic adaptation to various investment objectives. Under risk-minimization and return-maximization strategies, it distinctly favored Gasoline/Brent Crack Spread and Heating Oil/Brent Crack Spread respectively, showcasing its ability to calibrate between risk and return profiles. The prototype leverages parallelization to achieve up to 20-fold decrease in computational latency. This paper’s findings extend to the computational finance and artificial intelligence realm, presenting an efficacious machine learning-centered approach to portfolio management. The developed prototype demonstrates robustness, scalability, and efficiency under variable inputs and conditions suggesting its potential for widespread implementation in the investment advisory sector. Moreover, it sets a precedent for future research in the development of AI-driven financial advisory systems, which could involve exploration of other stochastic optimization algorithms, machine learning models, and hybrid models to enhance forecasting accuracy and efficiency in financial applications.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115122472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Malicious Requests to Protect Web Applications and DNS Servers Against SQL Injection Using Machine Learning","authors":"Afnan Mahmud Al Badri, S. Alouneh","doi":"10.1109/ICCNS58795.2023.10193085","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193085","url":null,"abstract":"According to the Open Web Application Security Project (OWASP), SQL injection is one of the top ten threats associated with web security issues. This type of attack takes advantage of web application vulnerabilities to cause substantial damages such as financial loss and privacy exposure. In addition, it uses the Domain Name System (DNS) recursion process to extract information stored within the DNS-targeted databases. In this paper, Response Policy Zones (RPZ) technique will be applied to protect DNS systems against data exfiltration. On the other hand, the machine learning algorithm AdaBoost will be used to detect this type of attacks. Our proposed tool will allow Web Application Firewall (WAF) systems to automatically recognize and distinguish between normal and malicious SQL queries. Our results have shown improved accuracy score and reduced false and positives alarm rates, and our tool is tested against well-known SQL injection benchmark datasets.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128285306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deployment of an autonomous fleet of UAVs for Assessing the NDVI of Regenerative Farming","authors":"Faris. A. Almalki, M. Angelides","doi":"10.1109/ICCNS58795.2023.10193565","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193565","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) have emerged rabidly as a new technology for crop farming promising to increase the productivity of a farm, on the one hand, and to reduce its farmer’s workload, on the other hand, through their integration with other technologies such as Machine Learning (ML), and Internet of Things (IoT). This article aims at developing a framework for deploying an autonomous fleet of UAVs for regenerative farming. Hence, the framework objective is twofold: firstly, is to develop an autonomous fleet of UAVs using Deep Reinforcement Learning (DRL), and, secondly, is to synchronize their flying and schedule their tasks using Ant Colony Optimization (ACO). The implementation of the framework shows great potential when using a set of indicators including, Normalized Difference Vegetation Index (NDVI), Mean Squared Error (MSE) and Received Signal Strength Index (RSSI), to assess its performance for Regenerative Farming.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subsurface Heat stress detection in plants using machine learning regression models","authors":"Soumya Konery Satheeshkumar, Christopher Paolini, M. Sarkar","doi":"10.1109/ICCNS58795.2023.10193174","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193174","url":null,"abstract":"The heat stress due to the progressive increase in the earth’s atmospheric temperature impacts plant growth endangering ecosystem quality and world food security. Such less favorable climatic conditions create greater risks that affect crop production as well as threatens food security. Tracking plant health can help mitigate the heat stress due to global warming. Technology plays a central role in mitigating the pressure the farming industry is facing. Smart sensor systems take advantage of machine learning algorithms and modern computer hardware to create a smart node that is tailored specifically for sensing applications. These sensor systems combine ML and IoT technologies to assist the agriculture industry with innovative ideas and technological advancements to increase yields and improve farm performance. Also, having a good machine learning model with good performance could help farmers and governments to make early decisions to mitigate the impact of environmental changes that reduce the quantity or quality of crops. In our research, we attempt to detect heat stress by using an in-situ ML-enabled subsurface sensor system in an agricultural field so that the device can independently make inferences. The sensor node is buried in the soil and we need a machine-learning model to predict the ambient temperature based on the soil characteristics data and the heat stress would be detected using the ambient temperature. In order to find a machine learning model, a publicly available dataset with the soil variables was evaluated for performance using different machine learning algorithms to predict the air temperature at the subsurface. This trained ML model would be deployed in an FPGA in the field that is equipped with soil sensors. The heat stress metric is based on the ambient temperature and duration of exposure to heat that would be wirelessly transmitted from the node. This paper discusses the development of the software framework for the subsurface wireless sensor node.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129460306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early alert of an outbreak based on event detection and tracking on social networks: The case of Covid-19 and meningitis","authors":"J. Thiombiano, Yaya Traoré, Sadouanouan Malo","doi":"10.1109/ICCNS58795.2023.10193638","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193638","url":null,"abstract":"In this paper, we propose an early warning approach for the occurrence of meningitis or Covid-19 epidemics based on the analysis of Twitter messages (tweets). To achieve this, we initially employed automatic classification techniques using Natural Language Processing and domain ontologies to filter the tweets. Subsequently, we utilized the metadata of the tweets to geolocate them and extract their publication dates. The real-time collection of tweets is facilitated through Kafka’s ecosystem.For the real-time identification of tweets, we trained SVM and CNN models. Our best results were obtained with the CNN model, which yielded an accuracy of 0.99. Tweets pertaining to infections are then geolocated by considering various key elements within the tweet that can provide information about the location.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121824375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Media Narratives of Covid-19 Pandemic and Misinformation","authors":"M. Habes, M. Elareshi, K. Tahat, D. Tahat","doi":"10.1109/ICCNS58795.2023.10193404","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193404","url":null,"abstract":"To great extent, social media becomes highly problematic for many users by sharing misinformation during early the Covid-19 outbreak. Yet, the dissemination of myths, rumors, and misinformation remained highly dominant. This paper examined the level of social media used among university students in Jordan (350 respondents), with a cross-sectional design and executed structured questionnaires for data gathering purposes. Applying the Structural Equation Modeling for statistical analysis and supported, findings indicated that social media was a strong source of both information and misinformation. It was used to gather information and share knowledge (e.g., unauthentic and unchecked information) about the outbreak. Finally, the behavioral intention indicated a strong, significant relationship with sharing unauthentic information.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129295589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Reflecting Surfaces-Assisted Cognitive NOMA in 5G/B5G Networks: Design, Architecture, Challenges, and Future Directions","authors":"Sharief Abdel-Razeq, H. Salameh, H. Al-Obiedollah, Wafaa AbuEid","doi":"10.1109/ICCNS58795.2023.10193187","DOIUrl":"https://doi.org/10.1109/ICCNS58795.2023.10193187","url":null,"abstract":"The fifth-generation (5G) and beyond 5G (5G/B5G) wireless communication networks have prompted many challenges related to massive connectivity, high-speed transmission, spectrum efficiency, and energy efficiency. Utilizing cognitive radio (CR) technology and the non-orthogonal multiple access (NOMA) techniques can significantly improve system usage and performance as they support many users by allocating non-orthogonal resources and enable efficient spectrum utilization at the same time. Specifically, Cognitive NOMA networks offer a spectrum-efficient approach that exploits the licensed frequency bands opportunistically by altering the operating characteristics based on the surrounding radio frequency environment. However, such spectrum sharing and using non-orthogonal resources should be throttled to protect the quality of service (QoS) of the primary users (PUs) in such networks. Consequently, regulating the secondary users’ (SUs) transmit power is necessary, which might be detrimental to SU functionality. Intelligent reflecting surfaces (IRS), on the other hand, have emerged as a potentially game-changing technology for communication systems in recent years owing to their ability to boost both energy efficiency (EE) and spectral efficiency (SE) simultaneously. By strategically rearranging the signal’s path with inexpensive passive reflecting devices, IRS may significantly increase the range of wireless networks. This survey provides an overview of the IRS hardware design and analysis of the current state-of-the-art Cognitive NOMA and IRS-assisted Cognitive NOMA networks. It also presents the most important design challenges for deploying such networks. The survey concludes with a discussion of outstanding questions and suggestions for further investigation in this domain.","PeriodicalId":158016,"journal":{"name":"2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132521478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}