Distributed applications are becoming more scalable and high performance computing systems are essential to realize the applications. High performance computing systems like cloud computing systems are equipped with a...
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Malware may be classified into various families according to several factors, such as the method of delivery to an infected computing system, behaviors performed by the malware on an infected system, or through the pr...
Malware may be classified into various families according to several factors, such as the method of delivery to an infected computing system, behaviors performed by the malware on an infected system, or through the presence of key characteristics which can be recognized through malware signatures. Additionally, a given malware family may comprise of many variants which perform similarly on an infected system, yet differ from each other in some discernible way. In this paper, we show that understanding this difference in malware behavior among variants of the same malware family is possible through analysis of Windows API system call sequences and their related frequencies. This allows for the identification of changes in malware variant behavior and illustrates the relationships between malware families.
Modern applications involve complex tasks such as machine learning and multimedia content. Resource-constrained devices are unable to guarantee real-time processing for time-critical applications. Therefore, Edge Comp...
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Osteoporosis is a significant public health concern, significantly increasing the risk of fractures. Early intervention is crucial for preventing fractures and improving patient outcomes. This study investigates the a...
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ISBN:
(数字)9798350354737
ISBN:
(纸本)9798350354744
Osteoporosis is a significant public health concern, significantly increasing the risk of fractures. Early intervention is crucial for preventing fractures and improving patient outcomes. This study investigates the application of machine learning for predicting osteoporosis risk in clinical settings. We utilize a comprehensive clinical dataset that includes demographics, health metrics, and bone density measurements. Various binary classification models, including multiple ensemble methods, are compared to evaluate their performance in predicting osteoporosis risk. This comparative analysis provides valuable insights into the strengths and weaknesses of each model for osteoporosis risk prediction. Also, we explore the impact of the Synthetic Minority Oversampling Technique (SMOTE) on prediction accuracy. SMOTE addresses class imbalance, a common challenge in healthcare data, potentially enhancing the model’s ability to identify individuals at high risk of osteoporosis. Our findings underscore the potential of machine learning to accurately identify individuals at high risk of osteoporosis, instilling confidence in the technology’s capabilities to improve clinical decision-making and facilitate early intervention for osteoporosis patients.
The field of multimedia video has shown a vigorous development trend in recent years. The massive heterogeneous data computing tasks generated by new multimedia applications cause user experience to face two major cha...
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ISBN:
(数字)9798350361261
ISBN:
(纸本)9798350361278
The field of multimedia video has shown a vigorous development trend in recent years. The massive heterogeneous data computing tasks generated by new multimedia applications cause user experience to face two major challenges: latency and energy consumption. The emergence of multimedia edge networks allows users’ computing tasks to be offloaded to edge servers for execution, which will greatly reduce communication delays. But on the contrary, multimedia edge networks also face problems such as limited communication and computing resources, unknown global system information and difficulty in multi-agent collaboration. In this paper, we first describe a multimedia edge network architecture. In this architecture, multiple edge servers use communication base stations to provide multimediacomputing services to a large number of users. Then a multi-agent collaborative decisionmaking model is proposed, which ensures that agents with different strategies can ultimately achieve consistent decisionmaking. Finally, we provide a distributed computing task scheduling algorithm based on deep reinforcement learning. A large number of simulation experiments have proven the superiority of this method in minimizing delay, energy consumption and convergence.
Scientific publications can be challenging for non-experts due to their complex concepts, technical terminology, and detailed descriptions of results. Interactive simulations can be used as a powerful way to communica...
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multimedia data production, such as audio, video, and acoustic measurements in large volumes and speed, has approached the scale of big data in many sensor networks. multimedia Sensor Networks (MSN) are popular becaus...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
multimedia data production, such as audio, video, and acoustic measurements in large volumes and speed, has approached the scale of big data in many sensor networks. multimedia Sensor Networks (MSN) are popular because of their capability to manage and integrate the different domains of sensor outputs, their representations, and the encoding of these measurements. Studies on generic and specific models for detection and tracing events in a sensor network have been established by multiple models. At the same time, there is still the requirement to handle these measurements from the Complex Dataset perspective. In the current world scenario, sensor networks are not limited to producing high-quality and diverse sensor readings but also large volumes at high speed. The most intuitive methods of handling them focus only on the interested range of measurement or store-for-later use. The possibility of data analysis in such an information-rich big data environment can be improved. In this study, we propose a model, “Multilink” that helps to align the multimedia data generated by a sensor network to a standard easy-to-process feature space with the incorporation of Machine Learning centric architecture.
The main objective of this study was to examine the relationship among perceived usefulness, technology factors, big data, digital marketing adoption and purchase intention in the UAE tourism sector. This study also e...
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ISBN:
(数字)9798350354737
ISBN:
(纸本)9798350354744
The main objective of this study was to examine the relationship among perceived usefulness, technology factors, big data, digital marketing adoption and purchase intention in the UAE tourism sector. This study also examined the mediating role of digital marketing adoption as well. For that reason, data was collected from the tourists visiting UAE. This research used a self-administered questionnaire for data collection and analysis. The data was collected using convenience sampling. The usable response rate was $\mathbf{5 2. 3 4 \%}$. The collected data was examined using Smart PLS. The findings of the study revealed that big data, perceived usefulness, and technological factors significantly impact digital marketing adoption. Further, digital marketing adoption has a positive impact on purchase intention as well. These findings are helpful for the management of the tourism sector of UAE to develop purchase intention among tourists.
As the Internet of Things (IoT) continues to expand, integrating interconnected devices into daily life, the threat landscape similarly evolves with increasing cybercrime activities. Among these threats, Trojan horse ...
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ISBN:
(数字)9798350354737
ISBN:
(纸本)9798350354744
As the Internet of Things (IoT) continues to expand, integrating interconnected devices into daily life, the threat landscape similarly evolves with increasing cybercrime activities. Among these threats, Trojan horse malware poses a significant risk to IoT systems by disguising itself as legitimate software, thereby gaining unauthorized access and control over networked devices. These intrusions are particularly challenging to detect in IoT environments due to the diverse and often less secure nature of connected devices, allowing malicious activities to persist undetected, leading to data breaches, device malfunction, or unauthorized data transmission to attackers. Addressing the inadequacies of traditional malware detection methods, this paper introduces a novel machine learning-based approach tailored to the IoT context. Leveraging a detailed dataset of 177,482 instances across 86 attributes, we evaluate the efficacy of various algorithms, including Naive Bayes (NB), J48 trees, and IBk to detect Trojan horse activities specifically in IoT devices. Our research comprehensively compares these methods, aiming to enhance detection accuracy and adaptability in dynamic IoT settings significantly.
This paper uses JSP technology developed based on Java and with good platform scalability. This system has the characteristics of easy maintenance and easy expansion. It puts forward the design and development ideas o...
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ISBN:
(数字)9798350393682
ISBN:
(纸本)9798350393699
This paper uses JSP technology developed based on Java and with good platform scalability. This system has the characteristics of easy maintenance and easy expansion. It puts forward the design and development ideas of interactive dynamic communication website to solve the problems of traditional communication methods. According to the actual situation of interactive dynamic communication website, carry out demand analysis and feasibility analysis. Use the principle of Java technology to build the multimedia remote information platform, start with the connection between the security monitoring part of the multimedia remote information platform and the server and passenger plane, break the traditional method, study the key principle and function of Java technology, and design the multimedia remote information security monitoring platform, The data are analyzed through experiments to find the most suitable platform to ensure the security of remote network communication, so as to ensure the security of multimedia remote data and information.
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