The Internet of things (IoT) is an active area in the current research community due to the improvement in mobile computing and wireless networks. Currently, the IoT is involved in many fields like smart cities, smart health monitoring, smart tracking, and smart factory; therefore, it is introducing new research opportunities and industrial revolutions. Smart health, in particular, is very important and trendy domain for researchers and practitioners due to its continuous monitoring of health of patients. The objective of smart health is to provide medical facilities to patients at anytime and anywhere. The smart health monitoring systems are mostly connected with the wireless network medium that is extremely vulnerable for threats. However various attacks are observed that can endanger these health monitoring applications and systems. These attacks include Denial of Service (DoS) Attack, Fingerprint and Timing-based Snooping, Router Attack, Select and Forwarding attack, Sensor attack and Replay Attack. In this paper, we discuss these attacks with their impact on health monitoring systems with some suggestive measures from our research findings.

Cloud Technology is a most challenging modern area in the field of modern technologies in which assets (e.g., CPU and capacity) can be rented and discharged by the clients through internet on-demand basis. The cloud computing has been giving virtual computing services to a little, medium and extensive industries, and services, for example, infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Cloud computing has a great combination with the agile software development as a research area. Many researchers worked in Agile Cloud field. The software industries are using the agile methodology for efficient software development need some platform to get quick feedback from the client. Therefore, the agile-cloud is a great combination for it but due to security reasons that directly influence the less adoption of cloud in software industries. This paper reports the survey results of software industries. The total of seven IT industries and many professionals was involved in this paper. However, this paper also contributes and reveals how existing issues can affect agile-cloud adoption for efficient software development. Furthermore, we do not find any type of survey conducted in Pakistan's software industries-related to cloud-agile adoption.

Assistance for doctors in disease detection can be very useful in environments with scarce resources and personnel. Historically, many patients could have been cured with early detection of the disease. The application of deep learning techniques in the fields of medical imaging, on large datasets, has allowed computer algorithms to produce as effective results as medical professionals. To assist doctors, it is essential to have a versatile system that can timely detect multiple diseases in the lungs with high accuracy. Over time, although many classifiers and algorithms have been implemented, however, deep learning models (i.e., CNN, Deep-CNN, and R-CNN) are known to offer better results. After a thorough literature review of the state-of-the-art techniques, this work applies various models such as MobileNet, DenseNet, VGG-16, EfficientNet, Xception, and InceptionV3 to the selected large dataset. The goal is to enhance the accuracy of these algorithms by experimenting with parameter optimizations. We observe that MobileNet produces better results as compared to other models. We implement a deep convolutional GAN to produce synthetic X-ray images containing various pathologies already included in the chosen imbalanced dataset namely NIH Chest X-ray containing 14 classes. The synthetic dataset contains 1193 samples belonging to five classes. We test the suggested model using evaluation measures like recall, precision, and F1-score, along with binary accuracy. The suggested deep learning model produces recall as high as 57%, binary accuracy as 93.4%, F1-Score as 0.553, and AUC as 81. After the inclusion of generated synthetic samples, the value of the F1-score becomes 0.582 resulting in a 5% increase. Though, Generative Adversarial Network (GAN) shows lower performance, however, we encourage further research and experiments to find the versatility of GANs in the field of medical imaging.