Foundation University Journal of Engineering and Applied Sciences
(HEC Recognized Y Category , ISSN 2706-7351)

Heart Diseases Prediction and Diagnosis using Supervised Learning

Wajiha Safat, Ijaz Hussain — Tue, 10 Jan 2023 00:00:00 +0000

The existing data for clinical diagnosis are often enlarged, but available tools are not efficient enough for decision making. Data mining techniques provide a user-oriented approach for clinical diagnosis and reduce risk factors. To improve clinical diagnosis, particularly for heart diseases, nine different data mining techniques have been applied for classification and clustering. We compare all these techniques for better prediction. Despite all recent research efforts, the literature lacks the application of multiple techniques on multiple data sets for heart disease prediction; which helps in decision making. In particular, this study is the augmentation of techniques for multiple data analysis by comparing four datasets with 14 attributes and a different number of instances. Another challenge is how to increase the accuracy of the decision-making process. Our research findings predict the better accuracy by using SMO and classification via regression for all data sets which shows the significant difference. Consequently, this research further helps to integrate the clinical decision support, thereby reducing medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient recovery.

Multiple Eye Disease Detection Using Deep Learning

Rashid Amin, Adeel Ahmed, Syed Shabih Ul Hasan, Habib Akbar — Tue, 10 Jan 2023 00:00:00 +0000

Human eyes are susceptible to various abnormalities due to aging, trauma, and diseases like diabetes. Glaucoma, cataracts, macular degeneration, and diabetic retinopathy are the leading causes of blindness worldwide. It is crucial to detect and diagnose these eye diseases early to provide timely treatment and prevent vision loss. Multiple eye disease detection through the analysis of medical images can aid in this process. The steps involved in the detection of multiple eye diseases using deep learning include image acquisition, region of interest extraction, feature extraction, and disease classification or detection. In this study, we proposed a model using deep learning algorithms, ResNetand VGG16, to detect eye diseases such as uveitis, glaucoma, crossed eyes, bulging eyes, and cataracts. We achieved a 92% accuracy rate using ResNet50 and 79% accuracy using the VGG16 model. By automating the detection process, we can save time for doctors and increase the accuracy and detection rate. The proposed model can be integrated into the healthcare system to assist in early diagnosis and effective treatment of eye diseases.

A Comparative Analysis of Fruits and Vegetables Quality Using AI-Assisted Technologies: A Review

Tue, 10 Jan 2023 00:00:00 +0000

Food quality is a major issue for society since it is a crucial guarantee not only for human health but also for society's progress and stability. The planting, harvesting, and storage through preparation and consumption, all aspects of food processing should be considered. One of the most important methods for managing fruit and vegetable quality is by using AI food quality evaluation techniques. Emerging technologies such as computer vision and artificial intelligence (AI) are thought to profit from the availability of massive data for active training and the generation of intelligent and operational equipment in real-time and predictably. The review helps provide an overview of leading-edge artificial intelligence and computer vision technologies that can help farmers in agriculture and food processing. In addition, the review presents some implications for the challenges and recommendations regarding the inclusion of technologies in real-time agriculture, policies, and substantial global investments. In addition, the fourth industrial revolution technologies of profound learning and computer vision robotics which are key to sustainability for food production is also addressed in it.

Country Level Social Aggression Using Computational Modelling

Saqib Iqbal, Ghazanfar Farooq Siddiqui, Lal Hussain — Tue, 10 Jan 2023 00:00:00 +0000

Computational modelling is emerging field to model the cognitive as well as social interactions between individual and society. Aggression is social evil which is instance response and its impact last for long time. Different societies have different norms and values based on ecological, environmental and cultural attributes so aggression level also varies among individuals and societies. Current study is based on psychological and temporal aggressive behaviour different individuals and societies in same habitat. In this paper we have proposed a frame work to model human social and psychological behaviors. Results are based on simulation which are according to our assumptions.

Behavioral Authentication for Smartphones backed by "Something you Process"

Nouman Imtiaz, Abdul Wahid, Syed Shabih Ul Hasan, Habib Akbar, Adeel Ahmed — Tue, 10 Jan 2023 00:00:00 +0000

Authentication of smartphone devices has been never so important nowadays. Machine learning techniques are not far behind to touch the new milestones of the latest and ever updating world. However, totally depending on machine learning will give you the scenarios of false user being accepted as true one and a true user being rejected as the false one, which can be devastating in some cases. Fifth factor of authentication “Something You Process” eradicates most of the cases of the false acceptance and false rejection, if used with the mentioned techniques. The novel approach applied here is the fifth factor combined with machine learning system and Behavioral authentication. The fifth factor is anti-shoulder surfing since the arithmetic operation is hidden by hand placed on the screen. After placing hand on the screen in such a way that it hides the code from others, the system shows the arithmetic operation and the processed calculation is performed in user’s mind. The pattern which is shown to the user is public, but machine learns the touch dynamics of the user along with his different postures including lying posture. The focus has been on the aspect of something that can be another layer or line of defense which can save the user’s authentication process. It results in decrement of false acceptance or false rejection upon unlocking of a smartphone device. This study deals with the postures of standing, sitting, and lying. The data is collected and the features are extracted in all of these positions.

Foundation University Journal of Engineering and Applied Sciences (HEC Recognized Y Category , ISSN 2706-7351)

Heart Diseases Prediction and Diagnosis using Supervised Learning

Multiple Eye Disease Detection Using Deep Learning

A Comparative Analysis of Fruits and Vegetables Quality Using AI-Assisted Technologies: A Review

Country Level Social Aggression Using Computational Modelling

Behavioral Authentication for Smartphones backed by "Something you Process"

Foundation University Journal of Engineering and Applied Sciences
(HEC Recognized Y Category , ISSN 2706-7351)