Penyimpanan data sulit dalam persekitaran berbilang awan

Pengkomputeran awan telah berkembang selama bertahun-tahun untuk mengatasi kekangan pengiraan dan penyimpanan peranti mudah alih. Kekangan storan boleh diatasi melalui storan awan dengan ketersediaan sambungan internet. Walaupun pengkomputeran awan mempunyai faedahnya, kebimbangan keselamatan mengenai data pengguna masih wujud. Data yang disimpan di dalam awan dihantar melalui Internet dimana pengguna tidak mempunyai kawalan dan terdedah kepada penyalahgunaan data yang disebabkan oleh perisian hasad atau serangan orang dalam. Kajian ini bertujuan untuk memastikan keselamatan data dalam berbilang awan awam dengan pelbagai jenis penyulitan, teknik penghirisan dan juga panduan penggnaan aplikasi tersedia untuk pengguna. Aplikasi yang dicadangkan akan menyulitkan data di mana berbilang algoritma penyulitan disediakan dan ia juga memberi pilihan kepada pengguna untuk memilih jenis teknik penghirisan yang hendak digunakan. Selepas fail disulitkan, fail akan dihiris kepada segmen- segmen kecil di mana ia akan menjalani proses perawakkan dan dimuat naik secara rawak pada berbilang storan awan yang berbeza. Pengguna yang masih baru dalam dunia digital akan dibekalkan dengan beberapa maklumat dan penjelasan ringkas tentang apa itu penyulitan dan cara teknik penghirisan berfungsi pada data mereka. Langkah keselamatan yang dilaksanakan akan menghalang penyalahgunaan data pada awan walaupun salah satu akaun awan digodam. Ia juga memberi pengguna kebebasan untuk memilih jenis penyulitan dan teknik penghirisan untuk digunakan pada fail peribadi mereka, memberikan lapisan keselamatan tambahan untuk mengelakkan data mereka daripada dicuri ataupun hilang

Aplikasi jual-beli dalam talian berasaskan web awan dengan analisis data (Buy-On-Behalf Service)

Pemodenan dan pasca-COVID-19 telah membawa revolusi digital yang signifikan dan memberi impak yang besar dalam kehidupan kita, terutama dalam bidang pembelian atas talian. Walaupun edagang telah menjadi perkara biasa, terdapat beberapa produk seperti kraf tradisional, kepakaran produk tempatan, dan produk borong yang hanya boleh dibeli dari kedai fizikal. Apabila produkproduk ini dijual dalam talian, perantara sering terlibat untuk mengaut komisen tambahan yang meningkatkan kos secara keseluruhan. Oleh itu, pembeli sering bergantung kepada rakan dan keluarga yang berada di kawasan tersebut untuk mendapatkan produk ini kerana perjalanan ke sana hanya untuk membeli produk adalah tidak praktikal dan mahal. Oleh itu, projek ini dibangunkan untuk memudahkan pengguna dalam mendapatkan produk yang diingini, terutamanya dari kedai fizikal yang tidak menawarkan perkhidmatan dalam talian. Projek ini adalah penambahbaikan daripada projek yang telah dilaksanakan dan akan dibangunkan sebagai aplikasi web kerana ia boleh diakses oleh sesiapa sahaja yang mempunyai sambungan Internet yang stabil dan tidak berminat untuk memuat turun aplikasi dalam telefon pintar. Platform ini akan membolehkan pengguna untuk membeli produk melalui individu yang akan membeli barang yang diminta dari kedai fizikal. Selain itu, aplikasi web ini akan diintegrasikan dengan pengkomputeran awan untuk memastikan aksesibiliti dan skalabiliti aplikasi. Dengan mengurangkan jurang antara pembelian dalam talian dan luar talian, projek ini berjanji untuk mengubah pengalaman membeli-belah sambil mencipta peluang baru untuk pembeli dan penjual

Dynamic Virtual Machine Allocation Policy for Load Balancing using Principal Component Analysis and Clustering Technique in Cloud Computing

The scalability and agility characteristics of cloud computing allow load balancing to reroute workload requests easily and to enhance overall accessibility. One of the most important services for cloud computing is Infrastructure as a Service (IaaS). There is a large number of physical hosts in a cloud data center for IaaS and it is quite difficult to arrange the allocation of the workload requests manually. Therefore, different load balancing methods have been proposed by researchers to avoid overloaded physical hosts in the cloud data center. However, fewer works have used multivariate analysis in cloud computing environment for considering the dynamic changes of the computing resources. Thus, this work suggests a new Virtual Machine (VM) allocation policy for load balancing by using a multivariate technique, Principal Component Analysis (PCA), and clustering technique. Moreover, PCA and clustering techniques were simulated on a cloud computing simulator, CloudSim. In the proposed allocation policy, a group of VMs were dynamically allocated to physical hosts. The allocation was based on the clusters of hosts according to their similar features in computing resources. The clusters were formed using PCA and a clustering technique based on variables related to the physical hosts such as Million Instructions Per Second (MIPS), Random Access Memory (RAM), bandwidth and storage. The results show that the completion time for all tasks has decreased, and the resource utilization has increased. This will optimize the performance of cloud data centers by effectively utilizing the available resources

COMPARING TOOLS PROVIDED BY PYTHON AND R FOR EXPLORATORY DATA ANALYSIS

To uncover the insight behind the data, a comprehensive analysis is needed. Exploratory Data Analysis (EDA) is one of practical data analysis that will guide how to reveal any hidden information in the data. By doing EDA, any pattern and issue in the data will be seen and eventually will lead the hypothesis. To do EDA, besides any basic statistic is needed, a good tool to simplify the analysis is also a consideration. Python and R as a famous programming language in the data science world provide methods to implement that analysis. This paper will show how to perform EDA by utilizing Python and R programming

A Parallel Apriori-Transaction Reduction Algorithm Using Hadoop-Mapreduce in Cloud

Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud. &nbsp

Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm

In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm

A systematic literature review on outlier detection in wireless sensor networks

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally

A Systematic Mapping: Exploring Internet of Everything Technologies and Innovations

The Internet of Everything (IoE) represents a paradigm shift in the world of connectivity. While the Internet of Things (IoT) initiated the era of interconnected devices, the IoE takes this concept to new heights by interlinking objects, individuals, data, and processes. Symmetry in IoE innovation and technology is essential for creating a harmonious and efficient ecosystem to ensure that the benefits are accessible to a broad spectrum of society while minimizing potential drawbacks. This comprehensive review paper explores the multifaceted landscape of the IoE, delving into its core concepts, enabling technologies, real-world applications, and the intricate web of challenges it presents. A focal point of this review is the diverse array of real-world applications spanning healthcare, smart cities, industry 4.0, agriculture, and sustainability. Previous works and examples illustrate how the IoE reshapes these domains, leading to greater efficiency, sustainability, and improved decision making. However, the transformative power of the IoE is accompanied by a host of challenges, including security and privacy concerns, interoperability issues, and the ethical implications of ubiquitous connectivity. These challenges are dissected in order to comprehensively understand the obstacles and potential solutions in the IoE landscape. As we stand on the cusp of an IoE-driven future, this review paper serves as a valuable resource for researchers, policy makers, and industry professionals seeking to navigate the complexities of this emerging paradigm. By illuminating the intricacies of the IoE, this review fosters a deeper appreciation for the transformative potential and the multifaceted challenges that lie ahead in the Internet of Everything era

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods

A Hybrid MCDM Approach Based on Fuzzy-Logic and DEMATEL to Evaluate Adult Obesity

Obesity and its complications is one of the main issues in today’s world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity