This paper discusses about the significance of selecting right requirements management tools. It's no secret that poorly understood user requirements and uncontrolled scope creep to many software project failures. Many of the application development professionals buy wrong tools for the wrong reasons. To avoid purchasing the more complex and expensive tool, the organization needs to be realistic about the particular problem for which they opt. Software development organizations are improving the methods, they use to gather, analyze, trace, document, prioritize and manage their requirements. This paper considers four leading Requirements Management tools; Analyst Pro, CORE, Cradle and Caliber RM, the focus is to select the appropriate tool according to their capabilities and customers need.

The main aim of this study is to develop a spell-checker system for Arabic language. This is done by investigating the viability of applying the radix search tree approach. Through this scientific research several shrubs that represent Arabic characters will be built through serialized tracking of characters word where it can be added to the dictionary and with a special mark in the node that contains the last characters from each word; on other side during searching process, every word can be tracked character by character according suitable path inside its shrub, Accordingly, correct word can be recognized if and only if searching process locates some leaves during the traverse of the shrub. Otherwise, the word will be considered incorrect.

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Healthcare is one of the emerging application fields in the Internet of Things (IoT). Stress is a heightened psycho-physiological condition of the human that occurs in response to major objects or events. Stress factors are environmental elements that lead to stress. A person’s emotional well-being can be negatively impacted by long-term exposure to several stresses affecting at the same time, which can cause chronic health issues. To avoid strain problems, it is vital to recognize them in their early stages, which can only be done through regular stress monitoring. Wearable gadgets offer constant and real information collecting, which aids in experiencing an increase. An investigation of stress discovery using detecting devices and deep learning-based is implemented in this work. This proposed work investigates stress detection techniques that are utilized with detecting hardware, for example, electroencephalography (EEG), photoplethysmography (PPG), and the Galvanic skin reaction (GSR) as well as in various conditions including traveling and learning. A genetic algorithm is utilized to separate the features, and the ECNN-LSTM is utilized to classify the given information by utilizing the DEAP dataset. Before that, preprocessing strategies are proposed for eliminating artifacts in the signal. Then, the stress that is beyond the threshold value is reached the emergency/alert state; in that case, an expert who predicts the mental stress sends the report to the patient/doctor through the Internet. Finally, the performance is evaluated and compared with the traditional approaches in terms of accuracy, f1-score, precision, and recall.

Current research in Internet of Things (IoT) is focused on the security enhancements to every communicated message in the network. Keeping this thought in mind, researcher in this work emphasizes on a security oriented cryptographic solution. Commonly used security cryptographic solutions are heavy in nature considering their key size, operations, and mechanism they follow to secure a message. This work first determines the benefit of applying lightweight security cryptographic solutions in IoT. The existing lightweight counterparts are still vulnerable to attacks and also consume calculative more power. Therefore, this research work proposes a new hybrid lightweight logical security framework for offering security in IoT (LLSFIoT). The operations, key size, and mechanism used in the proposed framework make its lightweight. The proposed framework is divided into three phases: registration, authentication, and light data security (LDS). LDS offers security by using unique keys at each round bearing small size. Key generation mechanism used is comparatively fast making the compromise of keys as a difficult task. These steps followed in the proposed algorithm design make it lightweight and a better solution for IoT-based networks as compared to the existing solutions that are relatively heavy weight in nature.