A Genetic-Fuzzy System Algorithm Method for the Breast Cancer Diagnosis Problem

Breast cancer is an important medical problem, especially for women, computer-aided medical diagnosis is very important in terms of prevention and early detection. This paper presents early detection of breast cancer using two methods, namely genetic algorithm and fuzzy inference system which will be used for early detection of breast cancer which will be used by doctors with computer assistance to obtain medical diagnosis of breast cancer in Indonesia. Our research shows that the diagnosis of breast cancer using these two methods has a high level of accuracy.

WeRoute: Route Optimization Web-Based System and Driver Mobile Application

This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

Mr. Dr. Health-Assistant Chatbot

Research says 60% of visits to a doctor are for simple small-scale diseases, 80% of which can be diagnosed at home using simple check-up. These diseases mostly include common cold and cough, headache, abdominal pains etc. Whereas, chat-bots in healthcare are highly in demand, which functioning can offer various services from symptom checking and appointment scheduling. Therefore, the purpose of the research aims to design, develop and evaluate a health-assistant Chat-bot Application entitled “MR.Dr.” that helps users to ask any personal query related to healthcare without physically available to the hospital. MR.Dr. is evaluated in term of usability. 30 respondents attended the survey of usability evaluation. In the system usability scale MR.Dr. achieved 87.6 % rating which means Grade A (excellent). User's feedback level was pretty satisfying where 24/7 service is the highest one.

Development of Attendance Monitoring System with Artificial Intelligence Optimization in Cloud

The creation of an attendance management system based on biometrics is proposed in this research. Keeping track of student attendance during lecture periods has proven to be a difficult task. Because human calculating creates errors and wastes a lot of time, the capacity to compute the attendance percentage becomes a key challenge. For this reason, a biometric-based attendance management system is being developed. This system uses a fingerprint device to take attendance electronically, and the attendance records are kept in a database. Following student identification, attendance is recorded. Artificial intelligence is also proposed as a component of the system. The system will aid in the reduction of errors and the more effective compilation of attendance data.

Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture

Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.

Alzheimer's Disease: A Survey

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.

Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.

Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing

Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.

Brain Computer Interface for Emergency Virtual Voice

Brain computer interface (BCI) is one of the thriving emergent technology which acts as an interface between a brain and an external device. BCI for speech communication is acquiring recognition in various fields. Speech is one of the most natural ways to express thoughts and feelings by articulate vocal sounds. The purpose of this study is to restore communication ability of the people suffering from severe muscular disorders like amyotrophic lateral sclerosis (ALS), stroke which causes paralysis, locked-in syndrome, tetraplegia and Myasthenia gravis. They cannot interact with their environment even though their intellectual capabilities are intact. Our work attempts to provide summary of the research articles being published in reputed journals which lead to the investigation of published BCI articles, BCI prototypes, Bio-Signals for BCI, intent of the articles, target applications, classification techniques, algorithms and methodologies, BCI system types. Thus, the result of detailed survey presents an outline of available studies, recent results and looks forward to future developments which provides a communication pathway for paralyzed patients to convey their needs.

A Comprehensive Review on Artificial Intelligence Techniques for Covid-19 Pandemic

The pandemic situation due to the emergence of Covid-19 presents various problems physically, economically and mentally for the individuals world-wide, therefore faster solutions with wider access is essential to solve the problems which aids as a support to the healthcare. This is made possible through the incorporation of Artificial Intelligence (AI) technology to handle the situation of pandemic. This paper aims to present a comprehensive re-view of the applications employed using AI for the problems faced during Covid-19 pandemic. The AI applications involved in screening, predicting, forecasting, neighborhood contact tracing and drug discovery of Covid-19 are addressed in this review. This review also presents detailed working of AI algorithms in each application. This paper helps the researchers with vivid information of AI applications of Covid-19 pandemic.