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                <text>Swachh Bharat Abhiyan&amp;amp;nbsp;(a cleanliness campaign) was initiated in 2014 under the regime of the NDA government. As the name signifies, the term ‘Swachh' denotes cleanliness in Sanskrit. In its nascent stages, the program attempted to ameliorate waste management – most predominantly open defecation and efficient utilization of resources. With the primary motive of having a hygienic surrounding environment for its people, the government of India initiated sundry cleanliness drives under this campaign. However, the conceptualisation and formulation of plans is one aspect – the praxis, i.e., the practical implementation of these plans is another important consideration altogether, which is perhaps the primary concern for the Government of India – for, if there is an inability to acquire mass involvement and participation then there is an automatic implication of failure. By having a comprehensive understanding of the motivational factors and attitudinal characteristics that members of a given community harbor – it would be relatively efficient at gaining mass involvement. Consequently, by addressing those factors and formulating plans on the basis of such fundamental facets there could be an exponential rate of success for such programs. Adding on to the literature and knowledge, which pre-exists within this domain, the subsequent research can aid in the better understanding of social perceptual phenomena and the successful implementation of programs related to campaigns addressing such issues. The study undertaken had a sample size of 1000 respondents. The findings of the paper suggest that the cumulative assessment across the concerned thematic factors (political, social, environmental, and communicational) apropos the initiative indicate an effective social (r)evolution with respect to the perceptual notions of the sample population involved.
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                <text>Liberal Studies; Vol. 5 No. 1 (2020): January–June 2020; 101-116</text>
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                <text>A Cleanliness Drive in India: Assessment on Its Psycho-Social Impact</text>
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                <text>Liberating third world, Muslim women from the clutches of oppressive Arab men has proven to be a popular and seductive justification for the military invasion of various Middle Eastern states throughout history. Individuals that support the ‘liberation’ of women of colour through military occupations argue that women in these ‘third world states’ are oppressed and require the assistance of a western military invasion to facilitate their liberation. This stems from viewing Arab women as mainly docile and passive individuals rather than active participants of their lives and destinies engaging in their own forms of resistance. Yet, a growing number of scholars and feminists are beginning to question the possibility of liberating women through the very forces that oppress them- namely imperialism and militarism. This paper- through a case study of Iraq- is similarly critical of the possibility of liberating women through a military invasion. It attempts to answer questions such as; how are women living in ‘post-liberated’ Iraq versus under Saddam, and what do their daily struggles look like, in its endeavour to paint a picture of the status of the various groups of women in Iraqi society pre and post 2003 invasion. The paper argues Iraqi women experience their society and the Baath regime differentially based on a combination of factors such as their social class, place of residence- urban or rural, political affiliation and so on. Thus rather than viewing Iraqi women as a homogenous group of oppressed women that require western military assistance for their liberation the paper encourages individuals to pay more attention to the grassroots. Only when the oppressed and exploited participate in the process of their liberation can the endeavor be successful.</text>
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                <text>A closer look at the ‘misuses of feminism’ for advancing imperialistic agendas: A case study of Iraq.</text>
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                <text>Malwares are increasing rapidly. The nature of distribution and effects of malwares attacking several applications requires a real-time response. Therefore, a high performance detection platform is required. In this paper, Hadoop is utilized to perform static binary search and detection for malwares and viruses in portable executable files deployed mainly on the cloud. The paper presents an approach used to map the portable executable files to Hadoop compatible files. The Boyer–Moore-Horspool Search algorithm is modified to benefit from the distribution of Hadoop. The performance of the proposed model is evaluated using a standard virus database and the system is found to outperform similar platforms.</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 11 No. 2 (2017); pp. 113-127</text>
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                <text>Magazines like BusinessWeek and Time have confirmed â??The Power of Us</text>
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                <text>Nowadays, developing Virtual Reality (VR) and Immersive Virtual Reality (IVR) application is one of the alternative support tools used to support children in learning road safety education. At early stage of VR/IVR application development, cybersickness was one of the main concerns that occur in both non-immersive and immersive environment that include both VR platforms which are Mobile and Tethered. This is because it involves inconsistencies between visual and physical movement in the virtual environment. Thus, the primary concern of this study is to explore and identify appropriate design guidelines in reducing cyber and simulator sickness in developing VR/IVR application for both platforms which employed in previous research. In accordance, 14 studies have been selected for review in a comparative analysis. The findings of this study discuss the appropriate cybersickness reduction design guidelines and principle that applicable and beneficial in the development process of the VR/IVR application and respectively reducing the cybersickness effect</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 05 (2022); pp. 33-48</text>
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                <text>1865-7923</text>
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                <text>Virtual Reality</text>
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                <text>Indiana International &amp; Comparative Law Review; Vol. 8 No. 2 (1998); 231-260</text>
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                <text>Khattab M. Ali Alheeti</text>
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                <text>Abdulkareem Alzahrani</text>
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                <text>Maha Alamri</text>
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                <text>Duaa Al_Dosary</text>
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                <text>In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.</text>
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                <text>Copyright (c) 2023 Haider TH.Salim ALRikabi, Khattab M. Ali Alheeti, Abdulkareem Alzahrani, Maha Alamri, Aythem Khairi , Duaa Al_Dosary</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 11 (2023); pp. 131-140</text>
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                <text>1865-7923</text>
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                <text>Software Defined Network (SDN); Deep Neural Network (DNN); Machine Learning (ML); NSL-KDD;</text>
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                <text>A Comparative Study for SDN Security Based on Machine Learning</text>
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