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Full-Text Articles in Computer Sciences
On Hierarchical Clustering-Based Approach For Rddbs Design, Hassan I. Abdalla, Ali A. Amer, Sri Devi Ravana
On Hierarchical Clustering-Based Approach For Rddbs Design, Hassan I. Abdalla, Ali A. Amer, Sri Devi Ravana
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Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to …
Structure Estimation Of Adversarial Distributions For Enhancing Model Robustness: A Clustering-Based Approach, Bader Rasheed, Adil Khan, Asad Masood Khattak
Structure Estimation Of Adversarial Distributions For Enhancing Model Robustness: A Clustering-Based Approach, Bader Rasheed, Adil Khan, Asad Masood Khattak
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In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in isolation, our approach employs clustering algorithms in conjunction with dimensionality reduction techniques to group adversarial perturbations, effectively constructing a more intricate and structured feature space for model training. Our method incorporates density and boundary-aware clustering mechanisms to capture the inherent spatial relationships among adversarial examples. Furthermore, we introduce a strategy for utilizing adversarial perturbations to enhance the delineation between clusters, leading to the formation of more robust and …
A Proposed Artificial Intelligence Model For Android-Malware Detection, Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, Saed Alrabaee
A Proposed Artificial Intelligence Model For Android-Malware Detection, Fatma Taher, Omar Al Fandi, Mousa Al Kfairy, Hussam Al Hamadi, Saed Alrabaee
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There are a variety of reasons why smartphones have grown so pervasive in our daily lives. While their benefits are undeniable, Android users must be vigilant against malicious apps. The goal of this study was to develop a broad framework for detecting Android malware using multiple deep learning classifiers; this framework was given the name DroidMDetection. To provide precise, dynamic, Android malware detection and clustering of different families of malware, the framework makes use of unique methodologies built based on deep learning and natural language processing (NLP) techniques. When compared to other similar works, DroidMDetection (1) uses API calls and …
A Brief Comparison Of K-Means And Agglomerative Hierarchical Clustering Algorithms On Small Datasets, Hassan I. Abdalla
A Brief Comparison Of K-Means And Agglomerative Hierarchical Clustering Algorithms On Small Datasets, Hassan I. Abdalla
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In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering …
Trajectory Design For Uav-Based Data Collection Using Clustering Model In Smart Farming, Tariq Qayyum, Zouheir Trabelsi, Asad Malik, Kadhim Hayawi
Trajectory Design For Uav-Based Data Collection Using Clustering Model In Smart Farming, Tariq Qayyum, Zouheir Trabelsi, Asad Malik, Kadhim Hayawi
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Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to …
An Energy Efficient Routing Approach For Iot Enabled Underwater Wsns In Smart Cities, Nighat Usman, Omar Alfandi, Saeeda Usman, Asad Masood Khattak, Muhammad Awais, Bashir Hayat, Ahthasham Sajid
An Energy Efficient Routing Approach For Iot Enabled Underwater Wsns In Smart Cities, Nighat Usman, Omar Alfandi, Saeeda Usman, Asad Masood Khattak, Muhammad Awais, Bashir Hayat, Ahthasham Sajid
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Nowadays, there is a growing trend in smart cities. Therefore, Terrestrial and Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (TWSNs and IoT-UWSNs) are mostly used for observing and communicating via smart technologies. For the sake of collecting the desired information from the underwater environment, multiple acoustic sensors are deployed with limited resources, such as memory, battery, processing power, transmission range, etc. The replacement of resources for a particular node is not feasible due to the harsh underwater environment. Thus, the resources held by the node needs to be used …
A New Intra-Cluster Scheduling Scheme For Real-Time Flows In Wireless Sensor Networks, Gohar Ali, Fernando Moreira, Omar Alfandi, Babar Shah, Mohammed Ilyas
A New Intra-Cluster Scheduling Scheme For Real-Time Flows In Wireless Sensor Networks, Gohar Ali, Fernando Moreira, Omar Alfandi, Babar Shah, Mohammed Ilyas
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Real-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster scheduling does not consider that the clusters may have internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not utilize their empty time-slots and thus increase schedule length. In this paper, we propose a new intra-cluster scheduling algorithm by considering that clusters may have having internal or outgoing …
Towards An Efficient Data Fragmentation, Allocation, And Clustering Approach In A Distributed Environment, Hassan Abdalla, Abdel Monim Artoli
Towards An Efficient Data Fragmentation, Allocation, And Clustering Approach In A Distributed Environment, Hassan Abdalla, Abdel Monim Artoli
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© 2019 by the authors. Data fragmentation and allocation has for long proven to be an efficient technique for improving the performance of distributed database systems' (DDBSs). A crucial feature of any successful DDBS design revolves around placing an intrinsic emphasis on minimizing transmission costs (TC). This work; therefore, focuses on improving distribution performance based on transmission cost minimization. To do so, data fragmentation and allocation techniques are utilized in this work along with investigating several data replication scenarios. Moreover, site clustering is leveraged with the aim of producing a minimum possible number of highly balanced clusters. By doing so, …
Identifying Major Tasks From On-Line Reviews, Feras Al-Obeidat, Bruce Spencer
Identifying Major Tasks From On-Line Reviews, Feras Al-Obeidat, Bruce Spencer
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© 2017 The Authors. Published by Elsevier B.V. Many e-commerce websites allow customers to provide reviews that reflect their experiences and opinions about the business's products or services. Such published reviews potentially benefit the business's reputation, improve both current and future customers' trust in the business, and accordingly improve the business. Negative reviews can inform the merchant of issues that, when addressed, also improve the business. However, when reviews reflect negative experiences and the merchant fails to respond, the business faces potential loss of reputation, trust, and damage. We present the Sentiminder system that identifies reviews with negative sentiment, organizes …