The purpose of this study was to investigate MOOCs as an open gateway for ongoing learning opportunities to scholars, institutions, and academics. MOOCs are now being offered in the field of archives and records management (ARM) by several institutions. The justification was the opportunities to ARM institutions in Eswatini since constraints of inadequate ARM lecturers, inadequate education and training institutions, low skills levels, the paucity of funds, and limited infrastructure serve as barriers to the effective delivery of ARM education. This study provides the first perspective from a developing country like Eswatini. It explains the concept of MOOC and its present role in ARM, such that potential intercontinental collaboration could strengthen teaching and research. A survey design was adopted to collect the views of ARM professionals to assess their understanding and interest in MOOCs. The study revealed great interest among ARM professionals regarding further study through MOOCs, although the adoption of this learning method in Eswatini is still a rarity.

By transforming organic wastes into nutrient-rich bio-manure and minimising other environmental effects, composting enables the long-term management of organic wastes. Traditional methods of monitoring and process management present a number of challenges in terms of efficiently using available resources to produce high-quality compost. As a result, smart composting technologies must be introduced to make it feasible for small-scale units in urban areas as well as large-scale operations in outlying areas. By analyzing recent trends in digital-based design and development, the current study explores the reach of digitalization in bringing user-friendly solutions, such as the internet of things (IoT)-based rapid composter. Using sewage sludge and other organic wastes in a stainless steel concentric bin type adiabatic rapid composter with provisions for thermal control (glass wool), feeding and mixing, leachate recirculation, and an online data monitoring system (Arduino kit) using particular sensors, the composting trials were carried out. More than 25% of the control bin's temperature was preserved by the insulator. Within 28 days of treatment, stable and mature compost was produced as a consequence of the online monitoring system's observations of temperature, moisture content, and pH steering for the best aeration and rotating frequency.

By transforming organic wastes into nutrient-rich bio-manure and minimising other environmental effects, composting enables the long-term management of organic wastes. Traditional methods of monitoring and process management present a number of challenges in terms of efficiently using available resources to produce high-quality compost. As a result, smart composting technologies must be introduced to make it feasible for small-scale units in urban areas as well as large-scale operations in outlying areas. By analyzing recent trends in digital-based design and development, the current study explores the reach of digitalization in bringing user-friendly solutions, such as the internet of things (IoT)-based rapid composter. Using sewage sludge and other organic wastes in a stainless steel concentric bin type adiabatic rapid composter with provisions for thermal control (glass wool), feeding and mixing, leachate recirculation, and an online data monitoring system (Arduino kit) using particular sensors, the composting trials were carried out. More than 25% of the control bin's temperature was preserved by the insulator. Within 28 days of treatment, stable and mature compost was produced as a consequence of the online monitoring system's observations of temperature, moisture content, and pH steering for the best aeration and rotating frequency.

Over the past few years, computer-aided diagnosis (CAD) has been rapidly advancing, with numerous machine learning algorithms being developed to identify various diseases, including leukemia, a variety of cancer that damages the white blood cells (WBCs) and stays in the bone marrow and/or blood. Early leukemia detection is essential for effective therapy and patient lifesaving. The illness can be categorized into two basic forms—chronic and acute—creating a sum of four subtypes. The proposed study focuses on the two subtypes, Acute Lymphoblastic Leukemia (ALL), Multiple Myeloma (MM), utilizing the SN-AM dataset. ALL happens when it makes many lymphocytes, whereas MM results from an accumulation of cancer cells that stop the bone marrow from adding the healthy blood cells. Normally, defining leukemia subtypes was a time-consuming and error-prone manual process undertaken by qualified professionals. The suggested model, The processes of extracting the precise information from images of cells before identifying the cancer subtype using different algorithms and its techniques, notably CNN. The algorithm outperformed existing ML techniques. With an accuracy rate of 97.2%. The DCNN model's efficiency in identifying the cancer type in the bone marrow was demonstrated by the fact that it performed on par with well-known CNN architectures. The study's overall conclusions show the potential of CAD and deep learning methods in enhancing medical diagnosis and improving patient outcomes.

Mobile ad hoc networks (MANETs) are considered to a large number of applications. Routing protocols are considered to be the most important element of MANET. Large-scale use of mobile ad hoc networks requires rapid data transfer, including the least possible disruption of some applications. Network setup and routing protocols are very important and should be relevant to the user’s requirements. The previous ECMP and MCMP protocol systems have some drawbacks of time delay analysis and the mobile ad hoc network’s load balance analysis. The Proposed Sensitive Life-Time Transmitted Multi-Path Routing (SLTMR) protocol can provide the mobile ad hoc network functionality. The Proposed Sensitive Life-Time Transmitted Multi-Path Routing (SLTMR) protocol is used to reduce the amount of interface during mobile transmission, focusing on reducing the path length and increasing the route path lifetime MANET. New measurements are proposed based on reducing the time delay by 0.010 per second, less network load performance of 2100 kbps based on increasing the nodes and increasing throughput performance to 32,000 kbps maximum. A comparative analysis of the performance of these routing protocols is provided to support network applications.

The purpose of this study was to investigate MOOCs as an open gateway for ongoing learning opportunities to scholars, institutions, and academics. MOOCs are now being offered in the field of archives and records management (ARM) by several institutions. The justification was the opportunities to ARM institutions in Eswatini since constraints of inadequate ARM lecturers, inadequate education and training institutions, low skills levels, the paucity of funds, and limited infrastructure serve as barriers to the effective delivery of ARM education. This study provides the first perspective from a developing country like Eswatini. It explains the concept of MOOC and its present role in ARM, such that potential intercontinental collaboration could strengthen teaching and research. A survey design was adopted to collect the views of ARM professionals to assess their understanding and interest in MOOCs. The study revealed great interest among ARM professionals regarding further study through MOOCs, although the adoption of this learning method in Eswatini is still a rarity.

In recent decades, breast cancer has increased to become the world's second leading cause of death among women. Chronic pain, genetic abnormalities, skin issues, texture of the skin, and color (redness) all appear to be indications of BC. Benign and malignant cancer is the most common binary classifications. Clinicians may discover a method of treatment that is both comprehensive and reliable. Machine Learning (ML) approaches are increasingly being employed in the classification of breast cancer. It supports with highaccuracy classifications and fast calculation skills. The proposed research work examines a supervised learning technique for classifying breast cancer that uses four different classifiers: Boosted Tree, Bagged Tree, Logistic Regression (LR) and Artificial Neural Network (ANN). Also, this research work will compare and contrast the four classifiers, as well as assess the performance. Based on the performance metrics, the above classifiers are analyzed, in which the Artificial Neural Network results with the accuracy of 97.56 % when compared to other classifiers.

In this paper, we propose a low complex architectural design for hearing aid applications. In this, we recast the hearing aid using distributed arithmetic (DA), which enables the implementation of hearing aid without multipliers. The design is based on the distributed arithmetic based formulation of it. It is further shown that high order filters, which are required to implement high-speed hearing aid can be realized using only look-up-tables and shift-accumulate operations. A novel approach was proposed to replace the decimation filter of a hearing aid using multiplier less architecture with a single DA unit. By proper initialization, it is shown that low complexity hearing aid architecture can be obtained. The proposed distributed arithmetic architecture is implemented in ASIC SAED 90 nm technology. The application of hearing aid is implemented in Matlab Simulink and Xilinx system generator tool. The obtained results show 20 % less area delay product and 40 % less power delay product when compared with the existing architecture.

Web scraping is a technique to extract information from various web documents automatically. It retrieves the related contents based on the query, aggregates and transforms the data from an unstructured format into a structured representation. Text classification becomes a vital phase to summarize the data and in categorizing the webpages adequately. In this article, using effective web scraping methodologies, the data is initially extracted from websites, then transformed into a structured form. Based on the keywords from the data, the documents are classified and labeled. A recursive feature elimination technique is applied to the data to select the best candidate feature subset. The final data-set trained with standard machine learning algorithms. The proposed model performs well on classifying the documents from the extracted data with a better accuracy rate.

Cloud computing that has become a relevant and virtualization technology that uses the internet and central remote servers to offer the sharing of resources that include software, frameworks, applications and business processes to the market environment to succeed the extensible demand. In the standard environment, the service vitality, elasticity, choices and flexibility suggest by this scalable technology are too attractive that makes the cloud computing to increasingly becoming a necessary part of the enterprise computing environment. This paper exhibits a survey of the current state of Cloud Computing. It involves in discussion of the evolution process of cloud computing, features of Cloud, current technologies adopted in cloud computing. This paper also exhibits a comparative study of cloud computing platforms (Amazon, Google and Microsoft and Cloudsim) and it stimulates the demands.