Efficiency of LSB steganography on medical information

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder

Interactive website on information dissemination

A school is an educational institution for imparting knowledge to children. In an age where information acquisition about a school is assuming astronomical heights, the need for cost-effective and efficient information transmission methods cannot be overemphasized; hence the use of the website of a school to disseminate information is advised. This study examines the process of disseminating information on a school website using a college in the Northcentral of the six-geopolitical zone in Nigeria as a case study. A prior study of manually or locally dissemination of information in a school was carried out and its limitations are highlighted. A website that is able to handle processes like admission, comment, and newsletter has been analyzed and developed using hyper-text language, cascading style sheet, hypertext preprocessor. The study results in solving the information dissemination problem in the college with the development of an educational interactive website

The network structure of depressive symptomatology in Peruvian adults with arterial hypertension

"Background: Globally, arterial hypertension (AH) has increased by 90% over the last four decades, and has increased by 1.6% in Peru over the previous four years. Scientific evidence indicates the prevalence of depressive symptoms in patients with AH and its importance in the comprehensive evaluation of the adult for adherence to clinical treatment. Previous studies carried out in the Peruvian population with AH mostly report the prevalence and associations, but do not indicate which depressive symptoms are more relevant in patients with AH. This study involved a network analysis of depressive symptomatology in Peruvian patients with AH using network estimation. Network analysis is used in this study for analysis, control, and monitoring purposes. Method: A representative cross-sectional study at the national level, using secondary data from 2019 Demographic and Family Health Survey (ENDES) was performed. The sample used included men and women of age over 17 years diagnosed with AH and was able to respond to Patient Health Questionnaire-9 (PHQ-9). Results: The symptoms of depressive mood (bridging force and centrality) and energy fatigue or loss (bridge centrality) play an essential role in the network structure, as does the feeling of uselessness in terms of closeness and intermediation. Conclusion: The study highlighted the symptoms related to depressive mood and energy fatigue or loss as bridging symptoms, which could trigger a depressive episode in patients diagnosed with AH. The results will contribute to developing personalized treatments aimed at patients with specific depressive symptoms who have also been diagnosed with AH. The study analysis presents statistical coefficients of effect size (≤ 0,1 = small; > 0,1 to < 0,5 = moderate; ≥ 0,5 = large) to determine network connections.

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.publishedVersio

A Prediction Model for Bank Loans Using Agglomerative Hierarchical Clustering with Classification Approach

Businesses depend on banks for financing and other services. The success or failure of a company depends in large part on the ability of the industry to identify credit risk. As a result, banks must analyze whether or not a loan application will default in the future. To evaluate if a loan application was eligible for one, financial firms used highly competent personnel in the past. Machine learning algorithms and neural networks have been used to train class-sifters to forecast an individual's credit score based on their prior credit history, preventing loans from being provided to individuals who have failed on their obligations but these machine learning approaches require modification to solve difficulties such as class imbalance, noise, time complexity. Customers leaving a bank to go to a competitor is known as churn. Customers who can be predicted in advance to leave provide a firm an edge in client retention and growth. Banks may use machine learning to predict the behavior of trusted customers by assessing past data. To retain the trust of those clients, they may also introduce several unique deals. This study employed agglomerative hierarchical clustering, Decision Trees, and Random Forest Classification techniques. The data with decision tree obtained an accuracy of 84%, the data with the Random Forest obtained an accuracy of 85% and the clustered data passed through the agglomerative hierarchical clustering obtained an accuracy of 98.3% using random forest classifier and an accuracy of 98.1 % using decision tree classifier

Smart transit payment for university campus transportation using RFID card system

In the transportation business, we aim to be cost-efficient and effective in our customer service but with the traditional transit payment system, it is not so. Lately, transit companies all over the world are moving towards superior client service, nimbleness, receptiveness to necessities that diverge at a time scale that was absurd even two decades ago. The aim of this study was to create an electronic transit payment system that will allow for full pliability and solutions functionality that Covenant Universities and Nigerian transit companies should adopt to become more effective and efficient. We achieved this with the use of radio frequency identification (RFID) smart cards and card readers aiding a computer program that was programmed using C#. In addition, the program was simple and not expensive to implement in order to eliminate the mismanagement of ticket funds, loiter paper in bus stations, and so on. Together all this became our payment system

Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of Ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results

Applicability of MMRR load balancing algorithm in cloud computing

Cloud computing is now a modern model for managing, configuring, and accessing distributed computing systems around the network on a full scale. One of cloud computing's fundamental problems is the balancing of loads, which is essential for evenly distributing the workload across all nodes. Over the years, scholars have proposed various approaches in order to resolve this problem. Nevertheless, optimizations of task execution time, completion time, response time, and virtual machine resources (VMs) are still posing tremendous challenges. This study proposes a new load balancing algorithm, which combines maximum minimum and round robin algorithm (MMRR), so that tasks with long execution time can be allocated using maximum minimum and tasks with lowest execution task will be assigned using round robin. Cloud analyst tool was used to introduce the new load balancing techniques and a comparative analysis with existing algorithm was conducted to optimize cloud services to clients. The study findings indicate that Maximum Minimum Round Robin (MMRR) has brought significant changes to cloud services. The data center’s loading time is good from both Throttled and MMRR, but Round Robin was worst. MMRR performed better from the algorithms tested based on the whole response time and cost-effectiveness (89%). The study suggested that MMRR be implemented for enhancing user satisfaction in the cloud service

Future trends in mechatronics

Presently, the move towards a more complex and multidisciplinary system development is increasingly important in order to understand and strengthen engineering approaches for the systems in the engineering field. This will lead to the effective and successful management of these systems. The scientific developments in computer engineering, simulation and modeling, electromechanical motion tools, power electronics, computers and informatics, micro-electro-mechanical systems (MEMS), microprocessors, and distributed system platforms (DSPs) have brought new challenges to industry and academia. Important aspects of designing advanced mechatronic products include modeling, simulation, analysis, virtual prototyping, and visualization. Competition on a global market includes the adaptation of new technology to produce better, cheaper, and smarter, scalable, multifunctional goods. Since the application area for developing such systems is very broad, including, for example, automotive, aeronautics, robotics or consumer products, and much more, there is also the need for flexible and adaptable methods to develop such systems. These dynamic interdisciplinary systems are called mechatronic systems, which refer to a system that possess synergistic integration of Software, electronic, and mechanical systems. To approach the complexity inherent in the aspects of the discipline, different methods and techniques of development and integration are coming from the disciplines involved. This paper will provide a brief review of the history, current developments and the future trends of mechatronics in general vie