Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at ...the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.
3GPP 5G V2X technology promised some advanced vehicle safety driving use cases in the future. Vehicle to vehicle communications (V2V) is the pivotal enabler in 5G-V2X by allowing the exchange of ...Cooperative Awareness Message (CAM) over the Physical Sidelink Shared Channel (PSSCH). In this paper, we modeled the performance of the 5G V2V PSSCH capacity limits based on different CAM payload sizes and vehicle densities using M/G/1 queuing theory and Newton Raphson (N-R) method. Results proved that 5G V2V cell capacity demands is in the order of multiple Gbps. PSSCH message should be kept below 1500 bytes to ensure moderate capacity demands below 1 Gbps limit. For any given practical 5G V2V system vehicle densities, size of CAM data payload and generation times will influence capacity limits. System capacity increase with increasing PSSCH message sizes and number of vehicles arriving into the system, with PSSCH message sizes being the more dominant contributor. Average capacity growth is larger in the 1000 to 1500 bytes region of about 130% as compared to higher PSSCH message size regions. The results ultimately presents important understanding on the relation between different CAM payload sizes and the resultant PSSCH message sizes, and their impact on total system capacities.
Mobile data offloading is a highly promising approach in mobile networks that tackles network congestion at Base Stations (BSs) and greatly improves both the Quality of Service (QoS) and Quality of ...Experience (QoE) for users. It presents significant business opportunities for operators, particularly in light of the exponential growth in mobile data traffic and the ongoing digital transformation. To effectively uphold the desired levels of QoS and QoE in the elevation of escalating digitalization and the unprecedented surge in data traffic, this paper presents offloading through a diverse range of technologies such as data offloading through Small Cell Networks (SCNs), Wi-Fi offloading, Device-to-Device (D2D) offloading, and data offloading through Vehicular Ad-Hoc Networks (VANETs). The SCNs and Wi-Fi offloading involve migrating data traffic to the alternative infrastructure i.e. the small BS and the Wi-Fi Access Points (AP), respectively while D2D focuses on transferring data through the device without transversing the BSs. VANETs is the process of offloading data in vehicular scenarios that consist of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). Additionally, mobile data offloading from cellular BS is categorized into four main factors: energy consumption or energy awareness, economic considerations, user satisfaction, and network congestion. These factors play a crucial role in the ongoing adoption and implementation of mobile data offloading strategies. Different technologies utilize diverse techniques to tackle the challenge of offloading, aligning with their specific research objectives. This paper delves into the challenges and outlines future research directions in the field of mobile traffic offloading.
COVID-19 Self-Monitoring Tool (COV-SMT) is the research developed to address multiple issues in monitoring quarantined individuals due to COVID-19 infection. As COVID-19 is still highly infectious ...despite the availability of vaccines, the implementation of contactless Internet of Things (IoT) technology should be encouraged to minimize the need for medical staff to perform daily health checks and thus prevent them from being directly infected during checking. This research aims to develop an effective method to monitor quarantined individuals regarding their vital signs, such as body temperature, heart rate, and oxygen level. A contactless self-monitoring tool integrated with a stages algorithm is developed to monitor these quarantined individuals with the help of IoT technology. It can provide a consistent platform for patients or users to transfer information or data through networks, including personalized healthcare domains. COV-SMT is an effective tool to streamlet the overall process of taking measurements from quarantined individuals. It integrates multiple sensors into one tool while providing a better overall picture with its graphical presentation to help patients and medical staff better understand their health conditions.
Better vehicle safety prediction for potential road accidents ultimately will avoid human fatalities. Designing an accurate and effective pre-crash system to avoid front and back crashes or ...mitigating crash severity is the priority goal of any vehicle safety system. To improve crash prediction, vehicle context is collected to analyze the severity of a crash based on safe avoidance time between two vehicles. This work proposes a real time crash prediction based on TGFD Crash Severity Factor model for Periodic Safety Message dissemination protocol in VANET. Simulation results show promising improvements for dissemination of PSMs to the vehicles for prevention of road accidents in highway scenarios.
VANET-based Connected and Autonomous Vehicle technology has the potential to improve safety hence reduce road fatalities. Many simulations in VANET develop only message dissemination protocol without ...considering the safe time needed between moving vehicles and these are rather inaccurate for safety preventions because communications without an effective time gap between vehicles could still lead to collisions. Passenger vehicles avoidance time can be associated with L0 - L2 and L3 - L5 levels of autonomy, dependent on either human-induced deceleration braking or full automated system braking. Two passenger vehicle avoidance time models have been proposed. The results obtained shows marked differences in the needed avoidance time for safe vehicle braking to avoid a crash. A delay of 4s is suggested to maintain a suitable avoidance time between leading and following vehicles for CAVs in urban and highway roads.
