In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems.

This study aimed to determine an efficient framework that caters to the security and consumer satisfaction for digital wallet systems. A quantitative online survey was carried out to test whether the six factors (i.e., transaction speed, authentication, encryption mechanisms, software performance, privacy details, and information provided) positively or negatively impact customer satisfaction. This questionnaire was divided into two sections: the respondents’ demographic data and a survey on security factors that influence customer satisfaction. The questionnaires were distributed to the National University of Malaysia’s professors and students. A sample of 300 respondents undertook the survey. The survey results suggested that many respondents agreed that the stated security factors influenced their satisfaction when using digital wallets. Previous studies indicated that financial security, privacy, system security, cybercrime, and trust impact online purchase intention. The proposed framework in this research explicitly covers the security factors of the digital wallet. This study may help digital wallet providers understand the customer's perspective on digital wallet security aspects, therefore motivating providers to implement appropriately designed regulations that will attract customers to utilize digital wallet services. Formulating appropriate security regulations will generate long-term value, leading to greater digital wallet adoption rates.

The combination of internet of things (IoT) with environmental sensing and image processing device has opened a new era to monitor the health of plants. Classification of plant diseases in early stages using image processing and analyzing environmental sensing data not only helps farmers to get healthy plants but also maximize the production. To monitor and classify plant diseases IoT is essential to send images and give feedback on it. In this paper, a raspberry pi based IoT device is proposed which sends images of plants to classify diseases and updates environmental parameters like air temperature, humidity, soil moisture and pH in MySQL database in real-time. To segment the affected part of plant, k-mean cluster algorithm is used after performing preprocessing stage and converting into L*a*b color space. Multi-class support vector machine (SVM) is applied to categorize disease using fourteen types of features of color, texture and shape obtained when implementing gray level co-occurrence matrix where the system was able to classify with an accuracy of 97.33%. Thus, classifying diseases and analyzing environment parameters help farms to monitor plant growth efficiently for better production.

Early detection of Parkinson's disease (PD) is very crucial for effective management and treatment of the disease. Dopaminergic images such as Single Photon Emission Tomography (SPECT) using ¹²³ I-Ioflupane can substantially detect PD at an early stage. However, till today, these images are mostly interpreted by humans which can manifest interobserver variability and inconsistency. To improve the imaging diagnosis of PD, we propose a model in this paper, for early detection of PD using image processing and artificial neural network (ANN). The model used 200 SPECT images, 100 of healthy normal and 100 of PD, obtained from Parkinson's Progression Marker's Initiative (PPMI) database and processed them to find the area of caudate and putamen which is the region of interest (ROI) for this study. The area values of ROI were then fed to the ANN which is hypothesized to mimic the pattern recognition of a human observer. The simple but fast ANN built, could classify subjects with and without PD with an accuracy of 94%, sensitivity of 100% and specificity of 88%. Hence it can be inferred that the proposed system has the potential to be an effective way to aid the clinicians in the accurate diagnosis of PD.

Facial recognition is a prevalent method for biometric authentication that is utilized in a variety of software applications. This technique is susceptible to spoofing attacks, in which an imposter gains access to a system by presenting the image of a legitimate user to the sensor, hence increasing the risks to social security. Consequently, facial liveness detection has become an essential step in the authentication process prior to granting access to users. In this study, we developed a patch-based convolutional neural network (CNN) with a deep component for facial liveness detection for security enhancement, which was based on the VGG-16 architecture. The approach was tested using two datasets: REPLAY-ATTACK and CASIA-FASD. According to the results, our approach produced the best results for the CASIA-FASD dataset, with reduced HTER and EER scores of 0.71% and 0.67%, respectively. The proposed approach also produced consistent results for the REPLAY-ATTACK dataset while maintaining balanced and low HTER and EER values of 1.52% and 0.30%, respectively. By adopting the suggested enhanced liveness detection, architecture that is based on artificial intelligence could make current biometric-based security systems more secure and sustainable while also reducing the risks to social security.