Authors:Patricia Ghann, Emmanuel Dortey Tetteh, Kobby Asare Obeng, Muhyideen Elias Pages: 4 - 16 Abstract: Organizations now deal with massive amounts of data. Data is collected from various points such as hospitals, credit card companies, and search engines. After collecting this voluminous data, it is published and shared for research. Data that is collected may have sensitive information that might be used to identify an individual and consequently lead to privacy violations when published. To address this challenge, privacy-preserving data publishing (PPDP) seeks to remove threats to privacy while ensuring that the necessary information is released for data mining. Various techniques have been proposed to solve the problems associated with sensitive information. One such technique is k- anonymity. This technique is the best and very efficient. However, it also leads to loss of information, reduces data utility, and works well only with static tables. In this paper, we proposed a technique that addresses the challenges of K-anonymity known as the Bit-Coded-Sensitive Algorithm (BCSA). This algorithm is more efficient and effective and ensures that the privacy of the individual is preserved by avoiding disclosure, and linkages and at the same time ensuring high quality and utility of data. BCSA first identifies the source of data and based on that, uses bits to code sensitive data with a key. PubDate: 2022-12-07 DOI: 10.3991/ijes.v10i04.35023 Issue No:Vol. 10, No. 04 (2022)
Authors:Houssem Lahiani, Mahmoud Neji Pages: 17 - 31 Abstract: Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones. PubDate: 2022-12-07 DOI: 10.3991/ijes.v10i04.35163 Issue No:Vol. 10, No. 04 (2022)
Authors:Festinë Retkoceri, Florim Idrizi, Shpend Ismaili, Florinda Imeri, Agon Memeti Pages: 32 - 42 Abstract: Nowadays data is growing tremendously. Therefore, there is a great need to store and process data. The problem of Pattern Searching has different applications. When searching for text or words in computer application systems, Pattern searching is used to display the search results. The purpose of Pattern searching is to find text within another text. For example, searching for text in books will take a long time and is hard work. Using Pattern searching will save you time and effort. If similar words are found within the requested text, it will underline the word similar to what was requested, otherwise it does not display any matches if there are no similar words within a text. This paper presents comparisons of the speed of different Pattern searching algorithms, precisely the Naive, KMP, Rabin-Karp, Finite Automata, Boyer-Moore, Aho-Corasick, Z Algorithm algorithms. We will test the time complexity of these algorithms in the three programming languages C#, Java and Python using three different CPUs. According to the results that appear in this comparison, we are able to perform the comparison between the programming languages and the comparison between the CPUs used in this research. PubDate: 2022-12-07 DOI: 10.3991/ijes.v10i04.35295 Issue No:Vol. 10, No. 04 (2022)
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