Modern Science and Intelligent System (MSIS Press) is a scholarly publisher that manages peer-reviewed journals in computing, engineering, science, education and related interdisciplinary fields. Each journal maintains its own scope, editorial listings, archive and publication record within the MSIS Press platform.The platform supports manuscript submission, editorial assessment, author status services, reviewer reports,...
International Journal of Advanced Computing and Intelligent System (IJACIS) publishes peer-reviewed research on computing systems, artificial intelligence, data science, software,...
International Journal of Advanced Scientific Discoveries (IJASD) publishes peer-reviewed original research and reviews addressing significant scientific questions and interdisciplinary...
International Journal of Electrical and Computational Technologies (IJECT) publishes peer-reviewed work connecting electrical engineering, electronics, communications, control and...
International Journal of Modern Education Science (IJMES) publishes peer-reviewed research on teaching, learning, educational systems, assessment and responsible educational innovation.
IJACIS
Dementia affects over 50 million people globally, with numbers expected to triple by 2050. Traditional diagnostic methods lack early detection capabilities and realtime monitor, leading to delayed intervention and increased caregiver burden. Our study deploys an integrated dementia detection unit combined with wearable Internet of Things (IoT) and deep machine learning model for early identification and continuous monitor of dementia symptoms. A comprehensive system was designed incorporating three components namely: (a) a wearable IoT device using MAX30102 sensor and the ESP32 microcontroller for physiological data collection, (2) a hybrid CNN-LSTM deep learning ensemble, and (c) a mobile app interface for data visualization. The dataset comprised 1,510 patient records with 11 features including demographic, cognitive, and clinical variables. Data preprocessing involved missing value handling, categorical encoding, feature scaling, and SMOTE balancing to address class imbalance. The Multi-Layer Perceptron showed superior performance on the balanced dataset with 0.97 Accuracy, 1.00 Sensitivity, 0.94 Specificity, and 0.98 AUC-ROC. The wearable device successfully collected real-time physiological data including blood glucose, blood pressure, and cholesterol levels – with our app providing seamless integration between hardware and software components, enabling realtime monitor alert and prediction delivery. The integrated system yields a significant advancement in dementia care with early detection abilities, continuous monitor, and improved accessibility. The MLP model's exceptional performance, combined with the practical wearable implementation, provides a scalable solution for healthcare systems seeking to improve dementia diagnosis and management while reducing caregiver burden.
IJACIS
Lung cancer has since become the second largest direct cause of death when compared to other forms of cancer. Lung cancer is easily diagnosed via medical x-rays, MRIs and CT-scans. The use of learning schemes in identification of disease has become a veritable tool in medical frontiers. But, a major challenge with image classification – is attributed to the disease severity, and model’s ability to address feature enhancements in model designs for image processing. The models must capture intrinsic features with image enhancements in regions of interest – amongst other feats. Our study uses a median filter (AMF) with contrast enhancement (CLAHE) to normalize images, use a variation-mode decomposition as preprocessor to sustain multimodal image learning upheld by the Xception model. VMD handles complex image data to resolve mode mixing in its AMF and CLAHE. This is further upheld via localized context and hierarchical image representation in our CNN-based Xception model to yield improved training efficiency, model stability and generalization even with complex spatiotemporal dependencies inherent the image dataset. Our result shows the proposed method performs well in lung cancer detection, with significantly enhanced time convergence and reliability to yield a 1.000 Accuracy, 0.994 Precision, 0.989 Recall and 0.989 F1-score. Thus, the proposed model provides an effective image recognition and detection solution for lung cancer.
IJACIS
Businesses today seek to generate more revenue for their products and services. However, attrition or churn for products and services over the digital market space has continued to plague businesses to proffer consumers with various alternates and choices to services and products for competitive money’s worth. Today’s businesses must thus, seek to yield better value while meeting consumer needs cum demands through a retention strategy. Thus, our study utilizes the recency-frequency-monetization as retention model that explores SMOTEEN-based AdaBoost ensemble to study the impact of imbalanced dataset to identify consumer churn. With chi-square feature selection, our proposed model yields F1 0.9819, Accuracy 0.9882, Recall 0.9848, Precision 0.9886, and Specificity 0.9824 respectively with 9632-correctly identified and 15-incorrectly classified cases. Conversely, other scheme yields ranges of F1 [0.6305–0.8875], Accuracy [0.6432-0.8879], Precision [0.6320–0.8805], Recall [0.6128-0.8876] and Specificity [06370-0.8894] respectively. This shows that model yields enhanced performance, and that the explored RFM-retention strategy was found to curb churn and improve monetization.
IJACIS
In this paper, a unique one-stop smart application that integrates note-taking, selling, and purchasing features into a single platform is introduced. Developed with Flutter, MongoDB, NodeJS, and Express, the application provides an easy-to-use interface for smooth participation in e-commerce operations. Its unique feature is that it combines the features of a buyer, seller, and guest mode, making it easier for users to conduct transactions and effectively manage their accounts. The note-taking feature of the app, which is available in all other modes, makes it much easier for users to take notes while managing their purchases or shopping. The software protects user data by using SQL-Lite for local data storage, which lowers the possibility of unwanted access or data breaches. The app's architecture also permits scalability and future growth, with the intention of introducing a delivery portion that will generate revenue for authorized staff members. This development supports the app's objective of providing a complete solution for buyers and sellers, doing away with the need for numerous platforms for various tasks. Compared to cloud-based solutions, the app's usage of local data storage reduces its carbon footprint in terms of environmental impact. This emphasis on environmental sustainability is consistent with current developments in technology, where eco-friendly methods are becoming more and more important. Overall, this initiative offers a forward-thinking method of approaching digital commerce by providing a safe, adaptable platform that takes into account the sustainability of the environment and the changing demands of users.
IJASD
Background: Rabies is a fatal but preventable zoonotic viral disease of the central nervous system. In developed countries, rabies is mostly found in wild animals like bats, but the developing countries like Ghana, dogs still carry rabies, and most rabies deaths in people around the world are caused by dog bites. There are limited studies on rabies in the Techiman Municipality, Bono East Region of Ghana. The current study focuses on the incidence of bites and resultant rabies infectivity, management and control in Techiman South Municipality. The objective of this study is to detect the gaps in rabies control and prevention practices while providing information for mass education. Methods: The study design was cross-sectional with Excel Format for the sample collection from the two departments under Veterinary Service Directorate in Techiman that is Disease Investigation Farm (DIF)/Regional Veterinary Laboratory (RVL) and the Municipal Veterinary Clinic (MVC) all in Techiman. In all, 163 reported cases were collected. Data on dog bites and rabies cases in Bono East Region from January to December 2022 were also analysed. Results: One hundred and Sixty-three (163) cases of animal bites were reported from January to December 2022. The majority of the animals involved were dogs which were 158 representing 96.93% and 73.61% of dogs were not vaccinated. Complete data were available for 157 completed cases. The age range of reported cases was 11months-83 years with few unknown ages. Nearly twenty-five percent (24.54%) were children under 10 years, the majority being males representing 47.85% but slightly above females representing 47.23%. All cases of animal bites were reported first to health facilities before they referred to veterinary facilities. There was a rapid increase of reported cases in the last quarter of 2022 83/163. Recommendation: Ghana Health Service and VSD operated independent systems of zoonotic disease reporting, collected very different sets of information and did not have a common platform for data sharing and reconciliation. This needs to be addressed to ensure better disease information collection, management and dissemination. Animals suspected of having rabies on dying have their brain tissue examined at KCCR as DIF/RVL not having the standard equipment for rabies diagnosis. This calls for upgrading the DIF/RVL and also further improvement in the skills and/or equipment for rabies detection at DIF/RVL. This was the reason why most of the samples were not able to be summited to KCCR due to the high cost of transportation to Kumasi.
IJACIS
Classification is a key data analysis technique for developing predictive models from labeled data. This study compares the performance of four classification algorithms such as Decision Tree, Neural Network, Naïve Bayes, and Support Vector Machine, applied to the Australian weather dataset. The objective is to predict the next day’s rainfall based on the target variable RainTomorrow. Each model is trained and evaluated using a consistent preprocessing pipeline and evaluated with various performance metrics including accuracy, precision, recall, and F1-score. Our analysis reveals Support Vector Machine (SVM) as the top-performing classifier, offering practical insights for weather-based predictive modeling tasks.
IJACIS
The weather remains one of the challenges in a country's development. It remains a mystery that meteorologists are still discovering new insights to understand it and make it use to our advantage to develop our country. Developing a predictive analysis to predict the next-day rain was suggested to overcome this problem. This paper will provide a detailed explanation of the dataset's pre-processing, with a focus on data reduction, cleaning, and transformation. We'll also talk about handling class imbalance problems and their associated data transformation.
IJASD
This paper describes a step-by-step process of creating an insurance-based application that predicts insurance premium using Artificial Intelligence (AI) method of machine learning (ML). Artificial Intelligence technologies will have a high impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Nowadays, AI is being used in insurance claims processing, underwriting, fraud detection, anomaly detection and customer service. In this research paper, we are going to predict a driver's premium charges based on the driver's driving behavior. Here, we used telematics, machine learning, and deep learning technologies for predicting the insurance premium cost in the auto insurance space. In this research journey, we propose a hybrid framework architecture with multiple combinations of Supervised Learning (LR, DS, RFR and SVM) Algorithms and interpretability of AI model.