Archana R. Raut

This study proposes an opportunistic screening framework using dental panoramic radiographs combined with a deep learning model based on ResNet50. A dataset of 474 radiographic images was utilized, comprising normal and osteoporotic cases. The methodology includes preprocessing, feature extraction using transfer learning, and classification using fully connected layers. Detailed training configurations such as batch size, dropout, and optimization strategies are incorporated to enhance reproducibility. The proposed model achieved an accuracy of 96%, outperforming other deep learning architectures including VGG16, VGG19, DenseNet, and InceptionV3. The results demonstrate that dental radiographs can serve as an effective, non-invasive, and cost-efficient screening tool for early osteoporosis detection.

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable and time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of ML techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large numbers of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML centered priority-based self-organized MAC (ML-MAC) protocol to provide a priority-based transmission system to ensure the timely delivery of critical data packets. In this research, depending upon the predictions of the ML model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm outperforms the existing I-MAC protocol.

Leaf diseases pose a significant threat to the productivity and quality of mango crops, necessitating effective detection and management strategies. This study presents an automated system for the detection of mango leaf diseases using machine learning techniques. Using image processing methods to extract relevant features from leaf images, various machine learning models were trained to accurately classify common mango leaf diseases. This approach involved using a comprehensive dataset of diseased and healthy mango leaves, preprocessing images, and extracting features such as color, texture, and shape. Features were extracted using MobileNetV2 and EfficientNetV2. Feature fusion was performed using a dense layer. Principal component analysis was used to reduce dimensionality. These reduced features were then fed to a support vector classifier to classify the mango leaf images. Eight different classes were considered, including the seven most common diseases in mango leaves and one class for healthy ones. The proposed model achieved a remarkable accuracy of 99.83 %. These results demonstrate that machine learning models can achieve high accuracy in the early detection of mango leaf diseases. Implementing this system in agricultural practices can significantly help farmers in timely disease management, reducing crop losses, and improving mango production.

In Wireless Sensor Networks, providing a fast, reliable, and Quality of Service supporting data transmission is required for real time data transmission. Medium Access Control (MAC) protocol performs a significant role in attaining the fast delivery of emergency data packets. To address this issue, this paper has presented an effective protocol for real time data transmission in WSNs. Priority based transmission scheme with different probabilities during transmission by applying priority to data packets is employed in the proposed protocol. To improve network performance, machine learning algorithm is applied in the proposed system for allocating transmission channel for packet delivery to the destination according to their priorities. In comparison with the TDMA-MAC protocol for enhancing transmission efficiency the proposed RT-MAC protocol showed a 17% improvement in successful packets delivery ratio performance.

Summary: In the current age, wireless sensor networks (WSN) are growing the attention of researchers. WSNs are significantly used in a broad scope of real‐time monitoring applications. With ongoing advances and requirements in industrial emerging time‐critical applications, WSNs have been accordingly stretched out to help the reliable, time‐bound data delivery. In WSNs, node organization, resource limitations, topology varieties, providing delay guarantees for channel access, and transmission time barrier are the most significant challenges in real‐time handling traffic. Suppose critical data packets arrive from sensor nodes simultaneously. In that case, finding the optimal path for the transmission of data packets so that data are reached within time bounds, taking into account the current network scenario, is a more inspiring task. With this perspective, in order to provide a priority‐based transmission scheme, an improved adaptive media access control (MAC) protocol is proposed to support priority‐based channel access considering the current network scenario, which ensures time‐bounded reliable transfer of critical data packets. The proposed system uses an adaptive learning‐centered approach to find the optimum path for sending critical data packets in varying network scenarios. The proposed mission‐critical MAC (MC‐MAC) protocol is assessed arithmetically to offer reliable and time‐bounded transmission for emergency data packets.

According to some estimates, 50 percent of accidents are caused by poor road conditions and the failure to wear a helmet. A smart interactive robotic helmet with features such as a traffic adaptive music player, wireless bike authentication, and road hazard warning playing can be offered to prevent accidents and encourage people to wear helmets. When a traffic hazard is coming, the helmet will inform the rider, communicate with him if he is not wearing it, and perform wireless bike authentication to avoid theft. Because two-wheelers are used more frequently in India than four-wheelers, it necessitates chevalier care in terms of safety. In today's culture, safety, like security, is extremely vital. The goal of this anti-theft mechanism system is to create a vehicle safety and security system that is implanted integrating and altering existing components. This system is primarily comprised of three modules: gas sensing, obstacle detection, and anti-theft warning system, all of which are connected to an ATmega16 microprocessor. This paper presents an overview of a smart helmet having various security and safety-related features.

Wireless sensor network has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.

: Today, the entire world is facing a big medical calamity called COVID-19. It is caused by a member of the β group of coronaviruses named SARS-CoV-2. This virus spreads from person to person through droplets generated during coughing and sneezing from the human body. This outbreak was initially observed in Wuhan, China, and now it has spread from country to country. As per the data collected from Worldometer, about 210 countries and territories were affected by this disease. Globally, as of 25th August 2020, there have been 23,518,343 confirmed cases of COVID-19, including 810,492 deaths. The most affected countries include USA, Brazil, India, Russia and South Africa, having more than 6, 00,000 infected people as of 25th August 2020. This outbreak of coronavirus is increasing worldwide, and this situation not only prepares mankind for fighting today's challenge but is also preparing for such type of future outbreaks. This paper highlights the symptoms, preventive measures and future challenges of COVID-19 based on the data referred from the World Health Organization (WHO).