Multimodal E-Commerce: A Usability and Social Presence Investigation

This thesis investigates empirically multimodal socially interactive e-commerce interfaces. The overall hypothesis is that multimodal social interaction will improve the usability of e-commerce interfaces and increase the user‘s feeling of social presence, decision making and product understanding when compared to an equivalent non-multimodal socially interactive interface. The investigation consisted eight conditions in three experimental phases. The first experimental phase investigated non-socially interactive, static-socially interactive, and interactive-socially interactive interfaces (three conditions) using an e-commerce platform with a dependent sample of users (n=36). The second experimental phase continued with the comparative evaluation of a further two conditions based on the results of the first phase. An audio and an avatar-based socially interactive conditions were evaluated with two independent groups of users (n=18 for each group). The third experimental phase investigated three socially interactive conditions. These were text with graphics, auditory stimuli, and avatars. The results demonstrate that socially interactive metaphors in e-commerce interfaces improved the ability of users to use presented information effectively, make decisions in comparison to non-social or static social interactive interfaces. An avatar-based socially interactive e-commerce interface improved the user‘s social presence. A set of empirically derived guidelines for the design and use of these metaphors to communicate information in a socially interactive atmosphere is also introduced and discussed

Social Distance Monitoring Approach Using Wearable Smart Tags

Coronavirus has affected millions of people worldwide, with the rate of infected people still increasing. The virus is transmitted between people through direct, indirect, or close contact with infected people. To help prevent the social transmission of COVID-19, this paper presents a new smart social distance system that allows individuals to keep social distances between others in indoor and outdoor environments, avoiding exposure to COVID-19 and slowing its spread locally and across the country. The proposed smart monitoring system consists of a new smart wearable prototype of a compact and low-cost electronic device, based on human detection and proximity distance functions, to estimate the social distance between people and issue a notification when the social distance is less than a predefined threshold value. The developed social system has been validated through several experiments, and achieved a high acceptance rate (96.1%) and low localization error (<6 m)

A new secure transmission scheme between senders and receivers using HVCHC without any loss

Abstract This paper presents a novel secure medical image transmission scheme using hybrid visual cryptography and Hill cipher (HVCHC) between sender and receiver. The gray scale medical images have been considered as a secret image and split into different shares by visual cryptography (VC) encryption process. The split shares are once again encoded by Hill cipher (HC) encode process for improving the efficiency of the proposed method. In this process, the encrypted medical image (shares) pixels are converted as characters based on the character determination (CD) and lookup tables. In result, a secret image is converted into characters. These characters are sent to the receiver/authenticated person for the reconstruction process. In receiver side, the ciphertext has been decoded by HC decode process for reconstructing the shares. The reconstructed shares are decrypted by the VC decryption process for retaining the original secret medical image. The proposed algorithm has provided better CC, less execution time, higher confidentiality, integrity, and authentication (CIA). Therefore, using this proposed method, cent percent of the original secret medical image can be obtained and the secret image can be prevented from the interception of intruders/third parties

Improving Pre-hospital Care of Road Traffic Accident's Victims with Smartphone Technology

One of the most causes to lose millions of lives around the world is Road Traffic Accidents (RTAs). According to the world health organization (WHO) report, 1.25 million people are killed each year as a result of RTAs, 20 to 50 million people were injured, and the number of killed people by RTAs is expected to increase further by 2020. The recent studies conclude that patient survival during a health emergency situation depends on the effective pre-hospital healthcare services, while the effective communication between the paramedics and prehospital staff is one of the important healthcare success factors. With the rapid growing of information and communication technology (ICT), wireless technologies and mobile services can provide viable solution to overcome the pre-hospital healthcare problems. The aim of this research is to improve the quality of prehospital emergency healthcare services at KSA by developing and implementing a mobile based emergency system. The proposed application is moving the diagnosis time to be started during traveling time witch accelerate the treatment. The proposed system shows satisfactory results in term of effectiveness and satisfactio

Improving Pre-hospital Care of Road Traffic Accident's Victims with Smartphone Technology

One of the most causes to lose millions of lives around the world is Road Traffic Accidents (RTAs). According to the world health organization (WHO) report, 1.25 million people are killed each year as a result of RTAs, 20 to 50 million people were injured, and the number of killed people by RTAs is expected to increase further by 2020. The recent studies conclude that patient survival during a health emergency situation depends on the effective pre-hospital healthcare services, while the effective communication between the paramedics and prehospital staff is one of the important healthcare success factors. With the rapid growing of information and communication technology (ICT), wireless technologies and mobile services can provide viable solution to overcome the pre-hospital healthcare problems. The aim of this research is to improve the quality of prehospital emergency healthcare services at KSA by developing and implementing a mobile based emergency system. The proposed application is moving the diagnosis time to be started during traveling time witch accelerate the treatment. The proposed system shows satisfactory results in term of effectiveness and satisfaction</p

Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient&rsquo;s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%

HLASwin-T-ACoat-Net Based Underwater Object Detection

Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25&#x0025; and 92.36&#x0025;, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments

Association between Obesity and COVID-19: Insights from Social Media Content

The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity