Wasswa Shafik | Yazd University (original) (raw)
Papers by Wasswa Shafik
IEEE Access, 2023
Plant pests and diseases are a significant threat to almost all major types of plants and global ... more Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI), machine learning (ML), or deep learning (DL) methods and models to provide disease detection solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review (SLR) and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five major academic databases, namely Springer, IEEE Xplore, Scopus, Google Scholar, and ACM library. After deploying a comprehensive screening process, the review considered 176 final studies based on the importance of the method. Several crops, including grapes, rice, apples, cucumbers, maize, tomatoes, wheat, and potatoes, have tested mainly on the hyperspectral imagery and vision-centered approaches. Support Vector Machines (SVMs) and Logistic regression (LR) classifiers demonstrated an increased accuracy in experiments compared to traditional classifiers. Besides the image taxonomy, disease localization is depicted in these approaches as a bottle neck to disease detection. Cognitive CNNs with attention mechanisms and transfer learning are showing an increasing trend. There is no standard model performance assessment though the majority use accuracy, recall, precision, F1 Score, and confusion matrix. The available 11 datasets are laboratory and in-field based, and 9 are publicly available. Some laboratory-based datasets are considerably small, making them impractical in experiments. Finally, there is a need to avail models with fewer parameters, implementable on small devices and large datasets accommodating several crops and diseases to have robust models. INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.
International Journal of Analytical Chemistry, Sep 21, 2021
e purpose of this research was to demonstrate the proximate analysis of poultry-mix made using ma... more e purpose of this research was to demonstrate the proximate analysis of poultry-mix made using maize bran as a basis. Red beans, soya beans, and benny beans were the three samples utilised in this study. is work investigates the appropriate poultry mix for birds breed for meat and egg. irty grammes of proteinous feedstock were weighed and homogeneously combined with 70 grammes of maize bran. e following was revealed in a proximate analysis of the feeds: moisture ranged from 1.18% to 1.54%, unrefined lipids 0.99-3.08%, total carbohydrate 57% to 72%, ash content 38.48% to 38.92%, unrefined protein 18.38% to 22.53% and unrefined fiber 2.0% to 4.65% respectively for broilers and layers. In terms of nutritional concentrations, all feed samples showed a substantial variation. Based on the findings of the study, it can be stated that Soya bean-maize bran is an excellent poultry-mix formulation that has deep well-disposed benefits and meets nearly all nutritional needs for meat and egg-producing birds.
Environmental science and engineering, 2023
International Journal of Advances in Applied Sciences, Mar 1, 2021
The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz ... more The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz bands. This unoptimized band because of the loss of mm-Wave bands, like path attenuation and propagation losses. Nonetheless, because of: (i) directional transmission paving way for beamforming to recompense for the path attenuation, and (ii) sophisticated placement concreteness of the base stations (BS) is the best alternative for array wireless communications in mmWave bands (that is to say 100-150 m). The advance in technology and innovation of unmanned aerial vehicles (UAVs) necessitates many opportunities and uncertainties. UAVs are agile and can fly all complexities if the terrains making ground robots unsuitable. The UAV may be managed either independently through aboard computers or distant controlled of a flight attendant on pulverized wireless communication links in our case 5G. Although a fast algorithm solved the problematic aspect of beam selection for 2-dimensional scenarios. This paper presents 3-dimensional scenarios for UAV. We modeled beam selection with environmental responsiveness in millimeter Wave UAV to accomplish close optimum assessments on the regular period through learning from the available situation.
Advances in information security, privacy, and ethics book series, Mar 27, 2023
Practice, progress, and proficiency in sustainability, Jun 30, 2023
The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (A... more The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in thi...
Advances in wireless technologies and telecommunication book series, Mar 31, 2023
International journal of computer applications, Mar 17, 2020
Reinforcement learning (RL) is a new research area practical in the internet of things (IoT) wher... more Reinforcement learning (RL) is a new research area practical in the internet of things (IoT) where it addresses a broad and relevant task through about making decisions. RL enables interaction of devices and with the environment through a probabilistic approach using the response from its own actions and experiences. RL permits the machine and software agent to attain its behavior constructed on feedback from the environment. The IoTs extends to devices to the internet like smart electronic devices that can network and interconnect with others over through connectivity of remote resources being supervised and meticulous. In this paper, we examine the main four RL techniques including Markov Decision Process (MDP), Learning Automata (LA), artificial neural network (ANN), Q-learning in relation to its applicability in IoT, challenges and link them to state of art solutions. This review provides a summarized analysis of RL techniques that researchers can use to identify current bottlenecks in IoT and suggest models that are in line with the move.
Journal of the Science of Food and Agriculture
BackgroundEarly plant diseases and pests identification reduces social, economic, and environment... more BackgroundEarly plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security.MethodologyAn enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes.ResultsThe study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble ...
International Journal on Smart Sensing and Intelligent Systems
Context With the rapid advancement of unmanned aerial vehicle (UAV) technology, ensuring these au... more Context With the rapid advancement of unmanned aerial vehicle (UAV) technology, ensuring these autonomous systems’ security and integrity is paramount. UAVs are susceptible to cyberattacks, including unauthorized access, control, or manipulation of their systems, leading to potential safety risks or unauthorized data retrieval. Moreover, UAVs encounter limited computing resources, wireless communication and physical vulnerabilities, evolving threats and techniques, necessity for compliance with regulations, and human factors. Methods This review explores the potential cyberthreats faced by UAVs, including hacking, spoofing, and data breaches, and highlights the critical need for robust security measures. It examines various strategies and techniques used to protect UAVs from cyberattacks, e.g., encryption, authentication, and intrusion detection systems using cyberthreat analysis and assessment algorithms. The approach to assess the UAVs’ cybersecurity hazards included STRIDE (a mod...
ICMAME 2023 Conference Proceedings
Mechatronic systems (MES) have been widely studied and integrated into current smart engineering ... more Mechatronic systems (MES) have been widely studied and integrated into current smart engineering systems like robots, and control systems among others due to advance in technology. These systems are widely intruded on during operation through sensor attacks and their associated drawbacks. A robust technique for identifying and preventing sensor attacks in systems such as drones must be implemented in smart transportation networks. This paper proposes a novel intrusion anomaly detection approach (IADA) for MES sensors using recurrent neural networks. F1-score and One class classification (CM) anomaly detection was used to carry out performance assessment on several countermodels and classifiers. The results demonstrated that the proposed detection achieved 96% of F1-score, 99% of sensitivity, and 92% of precision in comparison to other counterparts across several drone platforms. The future research direction of the proposed model is also depicted.
Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices
The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (A... more The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in thi...
Advances in information security, privacy, and ethics book series, May 26, 2023
IEEE Access
Plant pests and diseases are a significant threat to almost all major types of plants and global ... more Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI) or deep and machine learning methods to provide perfect solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five academic databases. After deploying a comprehensive screening process, the review considered 176 studies based on the importance of the method. Convolutional neural network (CNN) methods are typically trained on small datasets and are only intended for a few selected plant diseases. Finally, the lack of large-scale, publicly available datasets from the plant field is one of the main obstacles to solving plant disease identification and related problems, among others. INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.
Environmental science and engineering, 2023
JOIV : International Journal on Informatics Visualization, 2020
Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the... more Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designin...
International Journal of Data Warehousing and Mining
The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is co... more The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. ...
Wireless Communications and Mobile Computing
Devices are increasingly getting connected to the internet with the advances in technologies call... more Devices are increasingly getting connected to the internet with the advances in technologies called the Internet of Things (IoT). The IoTs are the physical device in which are embedded with software, sensors, among other technologies. Linking and switching data resources with other devices, IoT has been recognized to be a trending research arena due to the world’s technological advancement. Every stage of technology avails several capacities, for instance, the IoT avails any device, anyone, any service, any technological path or any network, any place, and any context to be connected. The effective IoT applications permit public and private business organizations to regulate their assets, optimize the performance of the business, and develop new business models. In this study, we scrutinize the IoT progress as an approach to the technological upgrade through analyzing traits, architectures, applications, enabling technologies, and future challenges. To enable an aging society, and o...
International Journal of Distributed Sensor Networks
The application of Big Data Analytics is identified through the Cyber Research Alliance for cyber... more The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable, and timely manner. An in-depth learning algorithm is utilized for training a neural network for detecting suspicious user activities. The proposed system architecture was implemented with the help of Splunk Enterprise Edition 6.42. A data set of average feature counts has been executed through a Splunk search command in 1-min intervals. All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. The rule-based method dispensed a low quantity of irregular illustrations in contrast with the Partitioning Around Medoids method. The results in ...
IEEE Access, 2023
Plant pests and diseases are a significant threat to almost all major types of plants and global ... more Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI), machine learning (ML), or deep learning (DL) methods and models to provide disease detection solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review (SLR) and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five major academic databases, namely Springer, IEEE Xplore, Scopus, Google Scholar, and ACM library. After deploying a comprehensive screening process, the review considered 176 final studies based on the importance of the method. Several crops, including grapes, rice, apples, cucumbers, maize, tomatoes, wheat, and potatoes, have tested mainly on the hyperspectral imagery and vision-centered approaches. Support Vector Machines (SVMs) and Logistic regression (LR) classifiers demonstrated an increased accuracy in experiments compared to traditional classifiers. Besides the image taxonomy, disease localization is depicted in these approaches as a bottle neck to disease detection. Cognitive CNNs with attention mechanisms and transfer learning are showing an increasing trend. There is no standard model performance assessment though the majority use accuracy, recall, precision, F1 Score, and confusion matrix. The available 11 datasets are laboratory and in-field based, and 9 are publicly available. Some laboratory-based datasets are considerably small, making them impractical in experiments. Finally, there is a need to avail models with fewer parameters, implementable on small devices and large datasets accommodating several crops and diseases to have robust models. INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.
International Journal of Analytical Chemistry, Sep 21, 2021
e purpose of this research was to demonstrate the proximate analysis of poultry-mix made using ma... more e purpose of this research was to demonstrate the proximate analysis of poultry-mix made using maize bran as a basis. Red beans, soya beans, and benny beans were the three samples utilised in this study. is work investigates the appropriate poultry mix for birds breed for meat and egg. irty grammes of proteinous feedstock were weighed and homogeneously combined with 70 grammes of maize bran. e following was revealed in a proximate analysis of the feeds: moisture ranged from 1.18% to 1.54%, unrefined lipids 0.99-3.08%, total carbohydrate 57% to 72%, ash content 38.48% to 38.92%, unrefined protein 18.38% to 22.53% and unrefined fiber 2.0% to 4.65% respectively for broilers and layers. In terms of nutritional concentrations, all feed samples showed a substantial variation. Based on the findings of the study, it can be stated that Soya bean-maize bran is an excellent poultry-mix formulation that has deep well-disposed benefits and meets nearly all nutritional needs for meat and egg-producing birds.
Environmental science and engineering, 2023
International Journal of Advances in Applied Sciences, Mar 1, 2021
The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz ... more The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz bands. This unoptimized band because of the loss of mm-Wave bands, like path attenuation and propagation losses. Nonetheless, because of: (i) directional transmission paving way for beamforming to recompense for the path attenuation, and (ii) sophisticated placement concreteness of the base stations (BS) is the best alternative for array wireless communications in mmWave bands (that is to say 100-150 m). The advance in technology and innovation of unmanned aerial vehicles (UAVs) necessitates many opportunities and uncertainties. UAVs are agile and can fly all complexities if the terrains making ground robots unsuitable. The UAV may be managed either independently through aboard computers or distant controlled of a flight attendant on pulverized wireless communication links in our case 5G. Although a fast algorithm solved the problematic aspect of beam selection for 2-dimensional scenarios. This paper presents 3-dimensional scenarios for UAV. We modeled beam selection with environmental responsiveness in millimeter Wave UAV to accomplish close optimum assessments on the regular period through learning from the available situation.
Advances in information security, privacy, and ethics book series, Mar 27, 2023
Practice, progress, and proficiency in sustainability, Jun 30, 2023
The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (A... more The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in thi...
Advances in wireless technologies and telecommunication book series, Mar 31, 2023
International journal of computer applications, Mar 17, 2020
Reinforcement learning (RL) is a new research area practical in the internet of things (IoT) wher... more Reinforcement learning (RL) is a new research area practical in the internet of things (IoT) where it addresses a broad and relevant task through about making decisions. RL enables interaction of devices and with the environment through a probabilistic approach using the response from its own actions and experiences. RL permits the machine and software agent to attain its behavior constructed on feedback from the environment. The IoTs extends to devices to the internet like smart electronic devices that can network and interconnect with others over through connectivity of remote resources being supervised and meticulous. In this paper, we examine the main four RL techniques including Markov Decision Process (MDP), Learning Automata (LA), artificial neural network (ANN), Q-learning in relation to its applicability in IoT, challenges and link them to state of art solutions. This review provides a summarized analysis of RL techniques that researchers can use to identify current bottlenecks in IoT and suggest models that are in line with the move.
Journal of the Science of Food and Agriculture
BackgroundEarly plant diseases and pests identification reduces social, economic, and environment... more BackgroundEarly plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security.MethodologyAn enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes.ResultsThe study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble ...
International Journal on Smart Sensing and Intelligent Systems
Context With the rapid advancement of unmanned aerial vehicle (UAV) technology, ensuring these au... more Context With the rapid advancement of unmanned aerial vehicle (UAV) technology, ensuring these autonomous systems’ security and integrity is paramount. UAVs are susceptible to cyberattacks, including unauthorized access, control, or manipulation of their systems, leading to potential safety risks or unauthorized data retrieval. Moreover, UAVs encounter limited computing resources, wireless communication and physical vulnerabilities, evolving threats and techniques, necessity for compliance with regulations, and human factors. Methods This review explores the potential cyberthreats faced by UAVs, including hacking, spoofing, and data breaches, and highlights the critical need for robust security measures. It examines various strategies and techniques used to protect UAVs from cyberattacks, e.g., encryption, authentication, and intrusion detection systems using cyberthreat analysis and assessment algorithms. The approach to assess the UAVs’ cybersecurity hazards included STRIDE (a mod...
ICMAME 2023 Conference Proceedings
Mechatronic systems (MES) have been widely studied and integrated into current smart engineering ... more Mechatronic systems (MES) have been widely studied and integrated into current smart engineering systems like robots, and control systems among others due to advance in technology. These systems are widely intruded on during operation through sensor attacks and their associated drawbacks. A robust technique for identifying and preventing sensor attacks in systems such as drones must be implemented in smart transportation networks. This paper proposes a novel intrusion anomaly detection approach (IADA) for MES sensors using recurrent neural networks. F1-score and One class classification (CM) anomaly detection was used to carry out performance assessment on several countermodels and classifiers. The results demonstrated that the proposed detection achieved 96% of F1-score, 99% of sensitivity, and 92% of precision in comparison to other counterparts across several drone platforms. The future research direction of the proposed model is also depicted.
Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices
The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (A... more The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in thi...
Advances in information security, privacy, and ethics book series, May 26, 2023
IEEE Access
Plant pests and diseases are a significant threat to almost all major types of plants and global ... more Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI) or deep and machine learning methods to provide perfect solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five academic databases. After deploying a comprehensive screening process, the review considered 176 studies based on the importance of the method. Convolutional neural network (CNN) methods are typically trained on small datasets and are only intended for a few selected plant diseases. Finally, the lack of large-scale, publicly available datasets from the plant field is one of the main obstacles to solving plant disease identification and related problems, among others. INDEX TERMS Convolutional neural networks, deep learning, image processing, machine learning, plant disease detections.
Environmental science and engineering, 2023
JOIV : International Journal on Informatics Visualization, 2020
Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the... more Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designin...
International Journal of Data Warehousing and Mining
The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is co... more The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. ...
Wireless Communications and Mobile Computing
Devices are increasingly getting connected to the internet with the advances in technologies call... more Devices are increasingly getting connected to the internet with the advances in technologies called the Internet of Things (IoT). The IoTs are the physical device in which are embedded with software, sensors, among other technologies. Linking and switching data resources with other devices, IoT has been recognized to be a trending research arena due to the world’s technological advancement. Every stage of technology avails several capacities, for instance, the IoT avails any device, anyone, any service, any technological path or any network, any place, and any context to be connected. The effective IoT applications permit public and private business organizations to regulate their assets, optimize the performance of the business, and develop new business models. In this study, we scrutinize the IoT progress as an approach to the technological upgrade through analyzing traits, architectures, applications, enabling technologies, and future challenges. To enable an aging society, and o...
International Journal of Distributed Sensor Networks
The application of Big Data Analytics is identified through the Cyber Research Alliance for cyber... more The application of Big Data Analytics is identified through the Cyber Research Alliance for cybersecurity as the foremost preference for future studies and advancement in the field of cybersecurity. In this study, we develop a repeatable procedure for detecting cyber-attacks in an accurate, scalable, and timely manner. An in-depth learning algorithm is utilized for training a neural network for detecting suspicious user activities. The proposed system architecture was implemented with the help of Splunk Enterprise Edition 6.42. A data set of average feature counts has been executed through a Splunk search command in 1-min intervals. All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. The rule-based method dispensed a low quantity of irregular illustrations in contrast with the Partitioning Around Medoids method. The results in ...