Many advantages to migrating different applications to cloud computing. These include a reduction... more Many advantages to migrating different applications to cloud computing. These include a reduction of cost, scalability of resources, reduction of business risks, and elimination of large number of Information Technology (IT) staff for management and administration. However, the main problem in cloud environment is still lack of standard approaches to identify cloud stakeholder’s roles. In this paper, we comprehensively study, identify, and classify multiple stakeholders’ roles of various types of reference architectures proposed in cloud computing domain.
Robotic manipulation refers to how robots intelligently interact with the objects in their surrou... more Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by rese...
Age estimation and gender detection are essential tasks in speech analysis and understanding, wit... more Age estimation and gender detection are essential tasks in speech analysis and understanding, with applications in various domains. Traditional approaches primarily rely on acoustic features extracted from speech signals, which may be limited by environmental noise and recording conditions. To address these challenges, we propose an improved approach that leverages multimodal speech data, combining audio, visual, and textual features for age estimation and gender detection. Our methodology includes a comprehensive analysis of multimodal features, a novel fusion strategy for integrating the features, and an evaluation of a large-scale multimodal speech dataset. Experimental results demonstrate the effectiveness and superiority of our approach compared to state-of-the-art methods in terms of accuracy, robustness, and generalization capabilities. This work contributes to the advancement of speech analysis techniques and enhances the performance of speech-based applications. This study ...
In an academic environment, plagiarism is the process of copying someone else's text, idea or... more In an academic environment, plagiarism is the process of copying someone else's text, idea or data verbatim or without due recognition of the source, which is a serious academic offence. Many techniques have been proposed in the literature for detecting plagiarism in texts, but only a few techniques exist for detecting figure plagiarism. The main problem associated with existing techniques of plagiarism detection is that they are not applicable to non-textual elements of figures in research publications. This paper focuses on detecting plagiarism in scientific figures. Textual-reference representation based figure plagiarism detection techniques are proposed and evaluated, based on existing limitations. The proposed techniques use enhanced feature extraction such as textual features and similarity computation methods such as similarity based on textual-reference of figures. The enhanced feature extraction method was found to be capable of extracting textual references such as captions and description texts. The similarity detection method was capable of categorising a given figure as either plagiarised or non-plagiarised from a source collection of scientific publications, depending on a certain threshold value. Results showed that the proposed technique achieved precision=0.78 and recall=0.67 result in terms of the evaluation measure.
Advances in intelligent systems and computing, 2017
Plagiarism is the process of copying someone else’s text or figure verbatim or without due recogn... more Plagiarism is the process of copying someone else’s text or figure verbatim or without due recognition of the source. A lot of techniques have been proposed for detecting plagiarism in texts, but a few techniques exist for detecting figure plagiarism. This paper focuses on detecting plagiarism in scientific figures. Existing techniques are not applicable to figures. Detecting plagiarism in figures requires extraction of information from its components to enable comparison between figures. Consequently, content-based figure plagiarism detection technique is proposed and evaluated based on the existing limitations. The proposed technique was based on the feature extraction and similarity computation methods. Feature extraction method is capable of extracting contextual features of figures in aid of understanding the components contained in figures, while similarity detection method is capable of categorizing a figure either as plagiarized or as non-plagiarized depending on the threshold value. Empirical results showed that the proposed technique was accurate and scalable.
IoT devices and embedded systems are deployed in critical environments, emphasizing attributes li... more IoT devices and embedded systems are deployed in critical environments, emphasizing attributes like power efficiency and computational capabilities. However, these constraints stress the paramount importance of device security, stimulating the exploration of lightweight cryptographic mechanisms. This study introduces a lightweight architecture for authenticated encryption tailored to these requirements. The architecture combines the lightweight encryption of the LED block cipher with the authentication of the PHOTON hash function. Leveraging shared internal operations, the integration of these bases optimizes area–performance tradeoffs, resulting in reduced power consumption and a reduced logic footprint. The architecture is synthesized and simulated using Verilog HDL, Quartus II, and ModelSim, and implemented on Cyclone FPGA devices. The results demonstrate a substantial 14% reduction in the logic area and up to a 46.04% decrease in power consumption in contrast to the individual d...
Muscular skeletal disorder is a difficult challenge faced by the working population. Motion captu... more Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the t...
Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare s... more Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare services. However, ensuring secure authentication and key agreement mechanisms is essential to protect sensitive patient data and maintain the integrity of the system. The existing methods face limitations in terms of vulnerability to cyber attacks, scalability, and resource utilization. Furthermore, the integration of blockchain technology introduces new complexities that need to be addressed. This research proposes an optimized fuzzy logic approach combined with blockchain technology to address the authentication and key agreement challenges in digital healthcare systems. The proposed solution leverages the flexibility and adaptability of fuzzy logic algorithms to handle uncertainty and imprecision in authentication decisions. By employing fuzzy logic, the system can effectively minimize false positives and false negatives, enhancing the robustness against adversarial attacks. Moreover,...
During the last few years, several approaches have been proposed to improve early warning systems... more During the last few years, several approaches have been proposed to improve early warning systems for reducing rock-fall risk. In this regard, this paper introduces a Deep learning-and (IoT) based Framework for Rock-fall Early Warning, devoted to reducing the rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused the prediction model-based deep learning with a detection model-based Internet of Things. In order to determine the framework’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and the ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, a framework reduced the risk probability from (1.51 ×10-3) to (8.57 ×10-9). Our results indicate the framework’s high...
The evolution of recent malicious software with the rising use of digital services has increased ... more The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before it impacts a large number of computers. Recently, many malware detection solutions have been proposed by researchers. However, many challenges limit these solutions to effectively detecting several types of malware, especially zero-day attacks due to obfuscation and evasion techniques, as well as the diversity of malicious behavior caused by the rapid rate of new malware and malware variants being produced every day. Several review papers have explored the issues and challenges of malware detection from various viewpoints. However, there is a lack of a deep review article that associates each analysis and detection approach with the data type. Such an association is imperative for the research community as it helps to determine the suita...
Many advantages to migrating different applications to cloud computing. These include a reduction... more Many advantages to migrating different applications to cloud computing. These include a reduction of cost, scalability of resources, reduction of business risks, and elimination of large number of Information Technology (IT) staff for management and administration. However, the main problem in cloud environment is still lack of standard approaches to identify cloud stakeholder’s roles. In this paper, we comprehensively study, identify, and classify multiple stakeholders’ roles of various types of reference architectures proposed in cloud computing domain.
Robotic manipulation refers to how robots intelligently interact with the objects in their surrou... more Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by rese...
Age estimation and gender detection are essential tasks in speech analysis and understanding, wit... more Age estimation and gender detection are essential tasks in speech analysis and understanding, with applications in various domains. Traditional approaches primarily rely on acoustic features extracted from speech signals, which may be limited by environmental noise and recording conditions. To address these challenges, we propose an improved approach that leverages multimodal speech data, combining audio, visual, and textual features for age estimation and gender detection. Our methodology includes a comprehensive analysis of multimodal features, a novel fusion strategy for integrating the features, and an evaluation of a large-scale multimodal speech dataset. Experimental results demonstrate the effectiveness and superiority of our approach compared to state-of-the-art methods in terms of accuracy, robustness, and generalization capabilities. This work contributes to the advancement of speech analysis techniques and enhances the performance of speech-based applications. This study ...
In an academic environment, plagiarism is the process of copying someone else's text, idea or... more In an academic environment, plagiarism is the process of copying someone else's text, idea or data verbatim or without due recognition of the source, which is a serious academic offence. Many techniques have been proposed in the literature for detecting plagiarism in texts, but only a few techniques exist for detecting figure plagiarism. The main problem associated with existing techniques of plagiarism detection is that they are not applicable to non-textual elements of figures in research publications. This paper focuses on detecting plagiarism in scientific figures. Textual-reference representation based figure plagiarism detection techniques are proposed and evaluated, based on existing limitations. The proposed techniques use enhanced feature extraction such as textual features and similarity computation methods such as similarity based on textual-reference of figures. The enhanced feature extraction method was found to be capable of extracting textual references such as captions and description texts. The similarity detection method was capable of categorising a given figure as either plagiarised or non-plagiarised from a source collection of scientific publications, depending on a certain threshold value. Results showed that the proposed technique achieved precision=0.78 and recall=0.67 result in terms of the evaluation measure.
Advances in intelligent systems and computing, 2017
Plagiarism is the process of copying someone else’s text or figure verbatim or without due recogn... more Plagiarism is the process of copying someone else’s text or figure verbatim or without due recognition of the source. A lot of techniques have been proposed for detecting plagiarism in texts, but a few techniques exist for detecting figure plagiarism. This paper focuses on detecting plagiarism in scientific figures. Existing techniques are not applicable to figures. Detecting plagiarism in figures requires extraction of information from its components to enable comparison between figures. Consequently, content-based figure plagiarism detection technique is proposed and evaluated based on the existing limitations. The proposed technique was based on the feature extraction and similarity computation methods. Feature extraction method is capable of extracting contextual features of figures in aid of understanding the components contained in figures, while similarity detection method is capable of categorizing a figure either as plagiarized or as non-plagiarized depending on the threshold value. Empirical results showed that the proposed technique was accurate and scalable.
IoT devices and embedded systems are deployed in critical environments, emphasizing attributes li... more IoT devices and embedded systems are deployed in critical environments, emphasizing attributes like power efficiency and computational capabilities. However, these constraints stress the paramount importance of device security, stimulating the exploration of lightweight cryptographic mechanisms. This study introduces a lightweight architecture for authenticated encryption tailored to these requirements. The architecture combines the lightweight encryption of the LED block cipher with the authentication of the PHOTON hash function. Leveraging shared internal operations, the integration of these bases optimizes area–performance tradeoffs, resulting in reduced power consumption and a reduced logic footprint. The architecture is synthesized and simulated using Verilog HDL, Quartus II, and ModelSim, and implemented on Cyclone FPGA devices. The results demonstrate a substantial 14% reduction in the logic area and up to a 46.04% decrease in power consumption in contrast to the individual d...
Muscular skeletal disorder is a difficult challenge faced by the working population. Motion captu... more Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the t...
Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare s... more Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare services. However, ensuring secure authentication and key agreement mechanisms is essential to protect sensitive patient data and maintain the integrity of the system. The existing methods face limitations in terms of vulnerability to cyber attacks, scalability, and resource utilization. Furthermore, the integration of blockchain technology introduces new complexities that need to be addressed. This research proposes an optimized fuzzy logic approach combined with blockchain technology to address the authentication and key agreement challenges in digital healthcare systems. The proposed solution leverages the flexibility and adaptability of fuzzy logic algorithms to handle uncertainty and imprecision in authentication decisions. By employing fuzzy logic, the system can effectively minimize false positives and false negatives, enhancing the robustness against adversarial attacks. Moreover,...
During the last few years, several approaches have been proposed to improve early warning systems... more During the last few years, several approaches have been proposed to improve early warning systems for reducing rock-fall risk. In this regard, this paper introduces a Deep learning-and (IoT) based Framework for Rock-fall Early Warning, devoted to reducing the rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused the prediction model-based deep learning with a detection model-based Internet of Things. In order to determine the framework’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and the ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, a framework reduced the risk probability from (1.51 ×10-3) to (8.57 ×10-9). Our results indicate the framework’s high...
The evolution of recent malicious software with the rising use of digital services has increased ... more The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before it impacts a large number of computers. Recently, many malware detection solutions have been proposed by researchers. However, many challenges limit these solutions to effectively detecting several types of malware, especially zero-day attacks due to obfuscation and evasion techniques, as well as the diversity of malicious behavior caused by the rapid rate of new malware and malware variants being produced every day. Several review papers have explored the issues and challenges of malware detection from various viewpoints. However, there is a lack of a deep review article that associates each analysis and detection approach with the data type. Such an association is imperative for the research community as it helps to determine the suita...
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