International Journal of Computer and Information Technology
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Business Process as a Service
Get PDFThe pursuit of adaptive and efficient operational strategies in contemporary corporate settings resonates with the inherent flexibility and transformative potential offered by cloud computing. The integration of cloud technology has emerged as a cornerstone for addressing modern business requirements and demands. In this context, the introduction of Business Process as a Service (BPaaS) has emerged as a critical link between the intricate landscape of business operations and the complexities of IT administration. This paradigm shift represents a forward-thinking and constructive development that has garnered increasing attention within academic and corporate circles.
The central focus of this research paper revolves around the intricate transition from traditional, often rigid, business process models to the dynamic and agile realm of cloud-based execution. This transition is marked by the adoption of a multi-tiered BPaaS architecture, meticulously designed to ensure that it aligns with cloud-centric features, underlying theoretical principles, and the latest technological frameworks.
The multi-tiered BPaaS architecture described herein offers a comprehensive classification system, providing a well-defined path for the prospective conversion of traditional business processes into a cloud-based service model. By meticulously examining each tier, this research paper seeks to unravel the nuanced intricacies, nuances, and practicalities of BPaaS. It serves as a foundational first step, offering a compelling starting point for future endeavors in the burgeoning field of BPaaS, both within academic discourse and corporate implementation.
Furthermore, this research paper anticipates that as BPaaS gains prominence and recognition in both academic and corporate spheres, it will catalyze the development of novel industry standards, thus contributing to the ongoing evolution of business processes in the digital age. The path laid out in this paper not only serves as a roadmap but also as a springboard for future growth, innovation, and adaptation in the ever-evolving landscape of business and technology
Fake Clothing Detection Using Deep Learning Method
Get PDFManufacture and distribution of fake clothing material which can be inferred to be criminal in nature has become a rapidly growing online shopping concern. It can be seen as a way of disguising false information as legitimate one. Indeed, many fashion industries face challenging times to meet market sales and expected profits once fake clothing products are sold on street corners. The consequences of clothing counterfeiting also range from huge losses to buyers and sellers of original products to health hazards, loss of image, and slow growth. More so, while IT has been beneficial, the introduction of IT has also provided a global platform for elusive counterfeiters and traders. The need for efficient/effective techniques for identifying or differentiating original clothing materials from fake ones is consequently on a geometric rise as well. This study developed and evaluated a fake cloth detection model using Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder using Python programming. The goal was to improve the capacity for discerning genuine and fake fabric items through image analysis. Dataset acquired from Kaggle was used for the training, testing, and validation phases in the ratio of 70:20:10 respectively. The processes includes resizing the images to a uniform size, converting them to grayscale or applying color normalization, and removing any irrelevant information. Data augmentation methods were applied to enhance the dataset\u27s diversity. Results obtained from the implementation of the model shows that the CNN model achieved perfect precision and accuracy, indicating that it performed well on the dataset. The RNN model achieved 97% precision while the Autoencoder model had a lower precision and accuracy compared to the CNN and RNN models. It correctly identified 63% of the positive instances, but its overall accuracy was 56%, indicating that it struggled with correct classification. These results also highlight the importance of selecting appropriate algorithms that align with the specific task requirements, especially as it found the autoencoder may excel in unsupervised learning scenarios, but its limitations become apparent in supervised classification tasks like fake cloth detection
Trends in Water Consumption Patterns Amongst Various Utility Users in Fort Portal City: Analysis of National Water and Sewerage Corporation (NWSC) [January 2009 to December 2015 data]
Get PDFThis study investigates trends in water consumption patterns among diverse utility users in Fort Portal City, Uganda, by analyzing data obtained from the National Water and Sewerage Corporation (NWSC) spanning the period from January 2009 to December 2015. The objectives of the study are threefold: 1) to analyze long-term water consumption trends, 2) to identify seasonal variations in water consumption, and 3) to assess the factors influencing water consumption.
The analysis of NWSC data reveals a consistent increase in overall water demand over the study period. This growth is attributed to factors such as population expansion, urbanization, and economic development within the city. Furthermore, we observe variations in water usage patterns among different user categories, with residential users showing steady growth and industrial users displaying fluctuations in demand.
Seasonal variations in water consumption are pronounced, with dry seasons witnessing heightened water use, particularly by residential and commercial users. These findings highlight the necessity for adaptive water management strategies to address peak demands during dry periods and advocate responsible water use practices.
Multiple factors influence water consumption, including population dynamics, economic activities, and NWSC policies and pricing structures. This necessitates a multifaceted approach to water resource management, tailored to the specific needs of diverse user categories.
The implications of this study underscore the importance of infrastructure development, water storage solutions, and resource allocation to meet the growing water demand in Fort Portal City. Public awareness campaigns, infrastructure maintenance, and collaborative efforts among stakeholders are recommended to promote responsible water use and equitable access to water services
Comparative Analysis on the Evaluation of the Complexity of C, C++, Java, PHP and Python Programming Languages based on Halstead Software Science
Get PDFQuality plays center stage in any software development industry. Software metrics have proven over time as the best measure to be used to assess and assure the software developers of the quality of their products. Halstead software science is an essential technique for measuring software complexity at the source code. In this study, we present a comparative study using this technique to help the developer by evaluating the code complexity by considering the structural composition of a programming language. In this study, an experiment was done using Halstead metrics to evaluate the complexity of PHP, C++, Java, C and Python programming languages. This study demonstrate that Halstead gives a better approach in evaluating the level of complexity of programming languages at source code level. The results showed that C++ and Java are the most complex programming languages while Python was the least complex warranting less of the programmer\u27s time and effort when developing a similar project. These findings can be used by the software developers to make decisions on the programming language to adopt when they want to come up with less complex software of high quality. In the future, the researchers will advance the study to incorporate other software paradigms and also modify the technique to capture also inter and intra-modular structural complexity of the various programming languages
Fortifying Connectivity: A Hybrid Algorithm Approach for Augmented Security and Efficiency in Bluetooth Technology
Get PDFBluetooth technology has become an integral part of our daily lives, providing wireless connectivity and seamless communication between wide ranges of devices. Bluetooth uses a master-slave architecture, where one device acts as the master, and the other devices act as slaves. The master device initiates and controls the connection, while the slave devices respond to connection requests from the master. In this research we are enhancing the security and efficiency of Bluetooth technology using hybrid approached algorithm i.e., combination of Two fish and ElGamal algorithm for making the communication process more secure and protected from foreign access. This research paper proposes a novel approach to enhance Bluetooth security by applying a hybrid algorithm that combines the strengths of Two fish and ElGamal encryption schemes. Two fish, a symmetric-key algorithm known for its high-speed data processing and resistance to attacks, will provide the foundation for encrypting data during Bluetooth communication. Concurrently, ElGamal, a public-key algorithm celebrated for its robust security and strong cryptographic properties, will complement the hybrid approach by ensuring secure key exchange between devices. The fusion of Two fish and ElGamal aims to overcome the limitations of using either algorithm in isolation, while capitalizing on their respective advantages to form a more potent and reliable security solution. By employing Two fish for efficient data encryption and ElGamal for secure key exchange, we anticipate a formidable defense against various attack vectors, including eavesdropping, man-in-the-middle attacks, and brute-force attempts.
Identification of Medical Mask Use by Applying the Convolutional Neural Network Algorithm and the Gabor Filter with Multiclass Classification
Get PDFSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) causes global pandemics and makes countries around the world lock down fortourists. This action is required to prevent the spread of viruses that take 14 days to disappear. SARS-COV-2 can easily infect individuals through a droplet. Thus, the governments of every country worldwide recommend wearing medical masks to prevent the spread of viruses, as well as maintaining distance during activities with others and washing hands frequently. Medical masks become efficient if their application is precise, owing to a lack of knowledge and self-awareness to preserve their distance and wash their hands. This paper proposes a Convolutional Neural Network (CNN) with Gabor filter implementation. The simulation uses a mask on a dataset with over 70,000 individual photos. The results demonstrated that the proposed CNN-Gabor model in this work could effectively classify the position of the mask when compared to the CNN model without the Gabor filter
Development and Implementation of Search Engine Optimization Algorithm using Angular Framework
Get PDFThe paper deals with the development of search engine optimization algorithm and its integration into Internet browsers. In particular, the paper presents an effective search engine optimization algorithm and the algorithm is implemented using a client-side programming angular framework. The research algorithm and its implementation make the search process efficient and flexible for the user, which is reflected in the fact that frequently searched phrases will be automatically suggested
Benchmarking Performances of Raspberry pi Microcomputer as Video Wall Devices
Get PDFVideo wall development is affected by cost, power consumption, processing capabilities, algorithm and video used. Literature has shown that using microcomputers reduces power consumption and cost, but performance remains a bottleneck. Benchmarking the performances of Raspberry pi (R-pi) devices with real-world loads will help understand R-pi video wall development, suitability and utilization. The approach used in this work is based on parallel video streaming using user datagram protocols (UDP) and broadcast addressing, while image splitting is done on clients. Nigel\u27s performance monitoring (NMON) tool was used with videos of varying frames 15fps, 20fps, 24fps, 25fps, 30fps, 50fps and 60fps and resolutions of 144p, 240p, 360p, 480p, 720p and 1080p to benchmark performances. Results revealed a maximum of 9.78%, 17.16%, 58.45 kB/s, and 1.13 kB/s for central processing unit (CPU), memory, network, and disk usage, respectively. Results also reveal that R-pi as a video wall device with the proposed approach has the processing capability towards enhancing video wall development. These results reveal for best performances, R-pi video walls are more suitable with videos of higher resolutions such as 480p, 720p and 1080p and at lower frame rates such as 24fps, 25fps and 30fps
A Recognition of Indonesian Traditional Cakes using The MobileNet Algorithm
No full textIndonesia is a country with a variety of cultures, ranging from dance to cuisine and food variations. Cake is one of the unique variations of food include traditional cake. A variety of custom-made cakes will make the taste special, even though the name is the same. Traditional cakes are foods that are part of the ancestral culture that has been passed down from generation to generation explicitly in the region or Indonesian society. Machine learning methods are suitable for consistent and clear object recognition, this requires complex image pre-processing and feature extraction methods. The proposed model of our research is MobileNetv2 which was customized and then we did fine tuning then all of our training datasets do data-augmentation to create new datasets with various patterns so that the train dataset can be more numerous and avoid overfitting and the model can detect cake differences with an accuracy rate of 94% and loss 0.06
Multi-Lingual Mobile Application to Improve the Pharmaceutical Care of Patients in Ethiopia
Get PDFDelivering essential information for patients concerning medication they’re taking is vital and improves healthcare services. Due to heavy burdens in every pharmacy, patients in Ethiopia do not get proper advice about the medicinal drugs they’re taking from the pharmacist. Moreover, it is far tough to recognize and interpret the various written instruction on remedy labels or package deal leaflet while not heaving the domain knowledge. As Ethiopia is an importer of many medicinal drugs from abroad, very often the package leaflets are not written in the local language. Therefore, due to language barrier, most of the patients cannot comprehend the information on medication labels and on package leaflet. In this regards most patients purchase the medication without a proper understanding of how to use the medication. This necessitates a technological based solution which serves patients in their language when they want an advice. This study demonstrates the design and implementation of a mobile-based solution to minimize such challenges and improve patient access to the medicinal drugs information. The requirements of the proposed system gathered using interview from pharmacist, doctors, technical expert, and client. Finally, a mobile application prototype is developed and evaluated for its effectiveness