Emotions are primarily thought of as mental experiences of body states, which are mostly shown in the face with precise and specific muscle patterns. It is, perhaps, the most critical attribute of living beings, and is extremely difficult to detect and generate artificially. Its detection always remains a well-explored classical problem. Existing approaches for detecting human emotions generally demand significant infrastructural overheads. Excluding these overheads, in this paper, we propose a much simpler way of emotion detection. To do so, We have induced different states of emotion through different multimedia components, and then collected participants' keystrokes (free text) and mouse usage data through a custom-developed survey. We have used several existing classifiers (KNN, KStar, RandomCommittee and RandomForest) and a newly proposed light-weight classifier namely Bounded K-means Clustering, to analyze those usage data for different emotional states. Our analysis demonstrates that emotion can be detected from the usage data up to a certain level. Moreover, our proposed classifier enables the best detection of five emotional states namely happiness, inspiration, sympathy, disgust, and fear compared to other existing classifiers. Besides, the analysis also reveals that user identification through usage dynamics does not result in a good level of accuracy when usage gets influenced by different emotional states.

Crowd management is a considerable challenge in many countries including Saudi Arabia, where millions of pilgrims from all over the world visit Mecca to perform the sacred act of Hajj. This holy ritual requires large crowds to perform the same activities during specific times, which makes crowd management both critical and difficult. Without proper crowd management and control, the occurrence of disasters such as stampedes, suffocation, and congestion becomes highly probable. At present, the internet of things (IoT) and its enabling technologies represent efficient solutions for managing and controlling crowd, minimizing casualties, and integrating different intelligent technologies. Moreover, IoT allows intensive interaction and heterogeneous communication among different devices over the internet, thereby generating big data. This paper proposes an intelligent IoT approach for crowd management with congestion avoidance in the Mina area, which is located in the holy city of Mecca. The approach implements a learning mechanism that classifies pilgrims based on the collected data and exploits the advantages of both IoT and cloud infrastructures to monitor crowds within a congested area, identify evacuation paths for pilgrims and guide the pilgrims to avoid congestion in real time. Moreover, the approach attempts to maximize crowd safety based on realistic scenarios by controlling and adapting pilgrim movements according to the characteristics of the possible hazards, pilgrim behavior, and environmental conditions. We evaluated our proposed approach by performing simulations based on real data sets and scenarios.

Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.