M. Al-Shalout, Khalid Mansour, Khaled E. Al-Qawasmi, M. Rasmi
{"title":"Classifying Date Palm Tree Diseases Using Machine Learning","authors":"M. Al-Shalout, Khalid Mansour, Khaled E. Al-Qawasmi, M. Rasmi","doi":"10.1109/EICEEAI56378.2022.10050426","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050426","url":null,"abstract":"One of Jordan's most significant agricultural crops is the date palm tree. The high level of interest in date palm farming is a result of the crop's superior economic viability when compared to other agricultural crops; Jordan's annual investments in this sector are expected to be more than $500 million. Recently, the Jordanian ministry of agriculture reported that many trees are vulnerable to damage because of several diseases related to date palms. In this study, the convolutional neural network (CNN) and support vector machine (SVM) algorithms are used to detect and classify date palm diseases. Four common diseases are considered in this paper: bacterial blight, brown spots, leaf smut, and white scales. The palm farms in the northern Jordan Valley, Kaggle, the National Center for Agricultural Research, and other sources provided the dataset used in this study. The experimental results show that CNN is effective mechanism for detecting and classifying Date Palm disease especially when large dataset is used in training the algorithm.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"1127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127776405","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":"Dynamic Modeling and performance analysis of an air conditioning system using Matlab/Simscape","authors":"A. Saleh","doi":"10.1109/EICEEAI56378.2022.10050462","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050462","url":null,"abstract":"This study aims to utilize Simscape/MATLAB tool potential to investigate the dynamic behavior of an air conditioning system and analyze the effects of many parameters on the performance of the system. Each component has a set of adjustable parameters that enables simulation of the real-time operation. The responses of the system to the variations in set point temperature and insulation levels are investigated and discussed. Improving the insulation level and increasing the set point temperature resulted in increased stopping periods and in decreasing the cooling load, thus reducing the energy consumed by the compressor and fans, and providing more suitable operating conditions. It was found that improving the level of insulation by replacing Ultra-High Performance Concrete with Foamed Concrete in the construction of the walls and the roofs and replacing the single-glazed windows with double-glazed ones resulted in energy savings up to 61.7%. It was also found that raising the set point value from 18 °C to 26 °C led to savings in energy consumption up to 23.4%. The results illustrated that Simscape is an efficient simulation tool and provides a comprehensive insight into the performance characteristics of air conditioning systems.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123155882","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}
Hani H. Attar, R. Agieb, A. Amer, A. Solyman, M. Aziz
{"title":"Microstrip Patch Antenna Design and Implementation for 5G/B5G Applications","authors":"Hani H. Attar, R. Agieb, A. Amer, A. Solyman, M. Aziz","doi":"10.1109/EICEEAI56378.2022.10050499","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050499","url":null,"abstract":"As demand for video downloads, wearable devices, and the Internet of Things (IoT) skyrockets, the next generation of communication systems will concentrate on attaining fast data transfer speeds and low energy consumption. These gadgets will be used for several applications, such as healthcare, environment monitoring, tourism, smart grid, intelligent traffic management, and agriculture. New applications need a fast transmission rate and bandwidth for data. The antenna is a crucial component inside communication systems that determines its bandwidth, data rate, and capabilities. Therefore, the authors of this work designed and modeled a microstrip patch antenna (MPA) for 5G and Beyond 5G (B5G) applications at 26 GHz frequency using millimeter wave bands. In this proposed design, CST software was used to design and model the antenna.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126426323","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}
K. Rezaee, Mohammad Hossein Khosravi, H. G. Zadeh, Mohammad Kazem Moghimi, G. Samara, Hani Attar, S. Almatarneh
{"title":"Diagnostic Tools for Detecting Autism Spectrum Disorder: A Review","authors":"K. Rezaee, Mohammad Hossein Khosravi, H. G. Zadeh, Mohammad Kazem Moghimi, G. Samara, Hani Attar, S. Almatarneh","doi":"10.1109/EICEEAI56378.2022.10050460","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050460","url":null,"abstract":"The autism spectrum disorder (ASD) is a developmental disability caused by abnormalities in the brain. Different methods have been used to diagnose ASD in the past, each with its own advantages and disadvantages. Prior research has focused mainly on algorithms and processing methods, rather than identification tools, to improve the automated diagnosis of ASD. This article identifies and describes diagnostic tools in the ASD literature, including facial features, EEG recordings, speech signals, and neuroimaging, in order to assist researchers interested in developing statistical, computational, and sound clinical approaches to ASD data mining. Accordingly, based on the responses obtained, this review study intends to assess several aspects of the key automatic diagnostic tools for autism spectrum disorders, including diagnostic accuracy, privacy protection, uncertainty, cost, efficiency, absence of methodological interference, and prospective clinical and therapeutic conditions.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130868884","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}
K. Rezaee, Mohammad Hossein Khosravi, Hani H. Attar, S. Almatarneh
{"title":"EEG-Based Driving Fatigue Recognition Using Hybrid Deep Transfer Learning Approach","authors":"K. Rezaee, Mohammad Hossein Khosravi, Hani H. Attar, S. Almatarneh","doi":"10.1109/EICEEAI56378.2022.10050453","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050453","url":null,"abstract":"One of the main causes of traffic accidents is driving while tired. World Traffic Safety Organization statistics indicate that driving while tired leads to 35-45% of road accidents and directly causes 1550 deaths, 71000 injuries, and 12.5 billion dollars in economic damage every year. To detect fatigue over time during driving, it is crucial to design an accurate and efficient analysis architecture. Physiological signals can significantly reduce the subjectivity and individuality of fatigue in drivers compared to methods like face analysis or questionnaire design. EEG signals contain valuable information about fatigue. Fatigue detection using brain signals is still a challenge due to the significant differences in EEG signals among people and the difficulty in collecting enough signal samples during fatigue. To reduce the level of classification error, hybrid deep learning was used in this paper as an efficient and fast method. By combining dilated-ResNet and Inception module, the approach is faster than other learning methods and provides satisfactory accuracy for fatigue detection. Windowing is used to receive signals from a limited number of channels. Windows are selected based on different lengths and then their spectrogram is created using short-time Fourier transform (STFT). More than 99% accuracy was achieved by the suggested approach. In order to recognize people's fatigue, the method has used a small number of channels that can be generalized and interpreted.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"39 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131923664","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":"Presenter List Countrywise","authors":"","doi":"10.1109/eiceeai56378.2022.10050488","DOIUrl":"https://doi.org/10.1109/eiceeai56378.2022.10050488","url":null,"abstract":"","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"4 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131741515","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":"ANFIS Based DC Offset Removal Technique for Numerical Distance Relaying","authors":"Ola A. Ananbeh, E. Feilat, Dia Abu Al Nadi","doi":"10.1109/EICEEAI56378.2022.10050497","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050497","url":null,"abstract":"In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is proposed to extract the fundamental component of the fault current utilizing the advantages of fuzzy logic and neural networks. The grey wolf optimizer algorithm (GWO) is utilized to estimate the fault current parameters. The proposed model is tested using both synthesized and simulated signals using Matlab software. The simulation is performed for different operating conditions by altering DC decaying current, time constant, harmonics, fault locations, fault resistance, and inception angels. The performance of the proposed technique is compared with that of the half cycle discrete Fourier transform (HCDFT) in the presence of the DC decaying current. The simulation results show that the ANFIS based technique accurately estimates the DC decaying current and extracts the fundamental component from the fault current within half a cycle following fault inception.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133470128","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":"Analyzing the Network System Performance Based on the Queuing Theory Concept","authors":"Omid Akbarzadeh Pivehzhani, Sahand Hamzehei, A. Amer, Nazanin Fasihihour, Mostafa Karami","doi":"10.1109/EICEEAI56378.2022.10050495","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050495","url":null,"abstract":"Increasing network use in several domains makes optimization more critical than ever before. The Queuing theory is used to comprehend the network's performance by comparing various factors to determine the optimal design based on the number of servers and distribution in order to maximize packet transmission and decrease packet loss. The result illustrates that uniform service time distribution, as opposed to an exponential distribution, may reduce packet loss. In addition, the behavior of packet transmission is depicted using servers with varying waiting line capacities. This study compares M/M/l, M/M/l/B, and M/M/l/B to determine the optimal arrangement for sending packets inside the network.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124295969","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":"Assessment of an Offshore Wind Farm Potential in the Gulf of Aqaba","authors":"M. Alzgool, Shatha Ghannam","doi":"10.1109/EICEEAI56378.2022.10050496","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050496","url":null,"abstract":"With renewable energy sources being the highlight of the energy generation sector in many countries in the past few years, Jordan with its ambitions targets to increase the contribution of renewable energy sources to the national energy supply. This paper aimed to investigate the potential and feasibility of designing an offshore wind farm in the Gulf of Aqaba - the only coastal city in Jordan -. In particular, this study investigates designing an offshore wind farm as a staged process involving: site selection, data gathering, calculation, preliminary design, and feasibility study. The analysis of the designed farm demonstrates acceptable potential; with the aid of the Weibull distribution function. The farm will cover the annual peak load in Aqaba, furthermore, it will be economically feasible due to the short payback period and the good annual income.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127272392","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":"Cyber Security of Mobile Applications Using Artificial Intelligence","authors":"Tariq Bishtawi, Reem Alzu’bi","doi":"10.1109/EICEEAI56378.2022.10050484","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050484","url":null,"abstract":"In recent years, the number of cyber-attacks has increased dramatically. Due to the widespread use of mobile devices, as well as the increasing popularity of mobile services, there are serious challenges in the field of cybersecurity. Traditional cybersecurity systems fail to detect malware and complex unknown attacks and do not guarantee user privacy is preserved. In the field of smartphone computing, artificial intelligence (AI) methods have expanded rapidly in recent years, often enabling devices to operate in an intelligent manner. Security against cyber-attacks on a large number of important mobile applications is a necessity in today's digital age. This paper presents how employees are using AI techniques to maintain cybersecurity in mobile applications using machine learning (ML), deep learning (DL), and artificial neural network (ANN). This paper describes an ANN to simulate a neuron in a mathematical equation, in which massive amounts of data are read to reach the desired result. ANNs are very useful in intrusion detection systems (IDS), mobile application security, and privacy breaches. We will also use supervised learning techniques in cybersecurity to identify malware and protect privacy.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114157469","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}