Journal of Informatics Electrical and Electronics Engineering (JIEEE)

A Study of Human Physiological Comfort in Lagos Metropolis Using Landsat Satellite Imagery Between 1984 and 2013

Uwadiegwu Ibeabuchi, Feyi Oni, Olusegun A. Adeaga — Sat, 25 Nov 2023 00:00:00 +0000

Our cities today are exposed more too urban atmospheric conditions that influence man’s comfort, health and behavior. As cities grow changes in land use have altered man’s physiological comfort experienced today. Landsat satellite imagery was used to study human physiological comfort in Lagos metropolis between 1984 and 2013. So, to achieve this remote sensing data acquired from Landsat satellite imagery and Geographic Information System (GIS) were used to derive, namely: land use land cover, land surface and air temperature, and relative humidity index for 1984, 2000 and 2013. While Temperature Humidity Index (THI) was computed from air tem-perature and relative humidity index which was used to assess human physiological out-door comfort in relation to land use activities. In this study, the health implica-tions of physiological stress and thermal sensation were highlighted and addressed from the environmental and health perspectives as it relates to land use. This study thrown open the door of environmental efficiency, well-being and the health of citi-zens which is influenced by physiological comfort through developing and maintain-ing comfortable environmental conditions that will enhance the quality of urban life.

Internet of Things (IoT)-based Smart Irrigation System for Sustainable Agriculture

Sat, 25 Nov 2023 00:00:00 +0000

The Internet of Things (IoT) is a collection of interconnected devices with self-configuring capabilities. Each aspect of the average person's daily life has been changed by the Internet of Things (IoT), which has made everything smart and intelligent. This paper proposes an Internet of Things (IoT)-based smart irrigation system for monitoring and managing field’s environment in real-time using cloud computing and various sensors connected with microcontroller. The system aims to reduce the time and energy of farmers by automating the process of monitoring field conditions and show the real-time measurement on mobile application and web application. The collected data is stored in the cloud and processed to facilitate automation through IoT devices. The results of the experimentation include temperature (DHT-11), humidity (DHT-11), soil moisture, water pump, fertilizer management (pH meter), and raindrop monitor. The system performs decision-making analysis with the interaction of the farmer and has the potential to increase crop productivity and reduce wastage of resources in agriculture sector.

Design of Smart Health Monitoring System for Disease Detection

Mukul, Vivek Verma — Sat, 25 Nov 2023 00:00:00 +0000

The most valuable asset is one's health. However, there isn't much free time for today's people to keep an eye on their health. A health monitoring system that tracks users automatically is necessary to alert users to their health status. Rapid advancement in internet and technology provides improvement in the health system. In the conventional method, people are required to visit their doctors on a regular basis for health checkups. The use of technology in the health monitoring system can save valuable time by having the health status automation. Additionally, the cloud, which revolutionized data trans-formation, contributes to the development of a better and more dependable health monitoring system. One of the most important technology-based systems has been the healthcare monitoring systems, humans struggle with the problem of premature mortal-ity because people with various ailments don't receive prompt medical care. The main objective was to create a trustworthy smart health monitoring system so that medical professionals could keep an eye on their patients, whether they were in a hospital or at home, using the proposed integrated healthcare system to ensure better and more dependable care. A prototype of health monitoring system is created, and it will have a number of cutting-edge features that will revolutionize the healthcare sector, with less reliance on human labor and greater efficiency in tasks like temperature monitoring, ECG-analysis, ambient temperature and humidity supervising, oxygen level recording, and BPM monitoring, this system will work autonomously. Body temperature, BPM, oxygen, temperature, and humidity sensors are included in the system to help maintain optimal health. The system's accuracy, reliability, sensitivity, usability, and scalability will be tested through comprehensive testing and validation in actual healthcare settings. This enables proactive and individualized health management. The results are anticipated to develop healthcare technologies and improve the standard of healthcare services.

Bitcoin and Cryptocurrency Exchange Market Prediction and Analysis Using Big Data and Machine Learning Algorithms

Adnan Branković, Dr. Jukić Samed — Sat, 25 Nov 2023 00:00:00 +0000

Due to economic uncertainty and the financial crisis of 2008, a desire for an unregu-lated currency arose, leading to the invention of Bitcoin. Using a pseudonym called Satoshi Nakamoto, Bitcoin was created in 2009, anonymously or by a group of un-known individuals. Since Bitcoin has been the most valuable cryptocurrency in recent years, its prices have fluctuated dramatically, making it difficult to predict their pric-es. Investors, businesses, risk managers, and market analysts can all benefit from being able to predict Bitcoin prices. By using the Bitcoin transaction data obtained from the Bitstamp website in this study, several different Machine Learning models are employed to determine the most accurate model for predicting Bitcoin prices. These models are based on 1-minute interval exchange rates in USD from January 1, 2012, to January 8, 2022. Analysis was performed primarily with Python, but it was also used and Hadoop, a distributed data storage and processing framework that uses the map-reduce programming model to allow efficient parallel processing of Big Da-ta. Based on the results of our research, comprising three experiments, autoregres-sive-integrated moving average (ARIMA) makes the most accurate prediction of Bitcoin prices, with a 95.98% success rate.

A Comparative Analysis of Emotion Detection Techniques

Abubakar Ali — Sat, 25 Nov 2023 00:00:00 +0000

Emotion recognition from facial expressions has become an urgent necessity due to its numerous applications in artificial intelligence, such as human-computer interface, marketing, mental health screening, and sentiment analysis, to name a few areas where emotion detection has become essential. In this paper we present a compara-tive analysis that offers insightful information about two techniques in emotion detection with CK+ and FER2013 datasets in deep learning, assisting researchers, practitioners, and policymakers in making defensible decisions about the selection and application of different methods in diverse applications. It emphasizes how important it is to continue researching and developing in the field of emotion detection in order to make it more reliable, accurate, and equitable in a variety of real-world situations. We focused on the two emotion detection techniques and databases employed, and the contributions that were dealt with. The Cascade Classifier algorithm and the Random Forest technique are thoroughly compared in this research to provide light on their advantages, disadvantages, and suitability for use in various fields. Additionally, the study evaluates the performance of both the Cascade Classifier and Random Forest algorithm on FER2013 and CK+ datasets, considering metrics such as accuracy, precision, f1-score, etc. Finally, the assessment of these methods incorporating the review measures is reported and discussed.

Implementing Cloud Security through AWS for Blood Bank Application

Apoorva Srivastava, Dr. Syed Wajahat Abbas Rizvi, Rashmi Priya — Sat, 25 Nov 2023 00:00:00 +0000

In today’s time the technologies are seeing a huge shift of work from the on-center to over the internet, which means that a huge number of online services is required, these services could be backup and the data recovery should be available over the internet. Cloud computing is the platform over the internet which provide the services of networking, sharing, storing, etc. on the internet. Not only this shift of data is being ob-served in IT sectors, but also this shift is being observed in the healthcare sector. With the increase in the average age limit of the human beings and new diseases increasing the medical needs. The traditional healthcare is now being replaced by modern and more progressive healthcare. Thus, cloud is providing with several healthcare solutions. How-ever, even after have a lot of benefits of using cloud services it also has some of the risks. The major risk being of the security of the patient’s data. This paper analyses the security issues and their possible countermeasures for the same. The security measures presented in the paper are based on patient data in relation to data storing, access and security of data. In the paper I have worked towards the development of the android application of blood bank, the application uses the Android studio as IDE and the AWS Amplify as the cloud service to host and deploy the application over the cloud. In this paper we will also see the benefits of using the cloud computing, its security and also what are the future works that need to be done in the field.

Optical Character Recognition Development Using Python

Prakhar Sisodia, Dr. Syed Wajahat Abbas Rizvi — Sat, 25 Nov 2023 00:00:00 +0000

Optical Character Recognition (OCR) is a technology used to convert scanned or digital images into editable text. OCR has become an increasingly important tool in the fields of data extraction and information retrieval, allowing for quick and efficient conversion of scanned documents and digital images into text. In this paper, we explore the use of the Python programming language to implement OCR algorithms and systems. We provide a comprehensive overview of existing Python libraries and packages used for OCR, including Tesseract and pytesseract, along with their strengths and limitations. We also examine the different OCR approaches and techniques, including template matching, feature extraction, and encrypting/decrypting the OCR parsed files and discuss their implementation in Python. Finally, we present a case study of a simple OCR system built using Python and evaluate its performance on a sample dataset. The results of our study highlight the potential of Python for OCR implementation and demonstrate its feasibility for real-world applications.