Computing software plays an essential role in almost every sector of the digital age, but the process of efficient software development still faces several challenges. Effective software development methodology can be the difference between the success and failure of a software project. This research aims to evaluate the overall impact of Agile Software Development (ASD) on the individual, organizational, software development, and project management dimensions. For this purpose, we surveyed several software development professionals from a variety of backgrounds (experience, location, and job ranks) to explore the impact of ASD on the IT industry of Pakistan. Our analysis of the collected information is two folds. First, we summarized the findings from our surveys graphically clearly show the opinions of our survey respondents regarding the effectiveness of the Agile methodology for software development. Secondly, we utilized quantitative measures to analyze the same data statistically. A comparison is drawn between the graphical and statistical analysis to verify the reliability of our findings. Our findings suggest the existence of a strong relationship between effective software development and the use of Agile processes. Our analysis shows that the job fit of software development professionals and ASD are critical factors for software development project success in terms of cost, quality, stakeholders satisfaction, and time. Although the study focuses on the IT industry of Pakistan, the findings can be generalized easily to other developing IT industries worldwide.

COVID-19 pneumonia is a persistent worldwide health problem that usually affects the most vulnerable groups in society: the newborn and aged populations. Most of the current endeavors toward handling diagnosis and classification of pneumonia have used numerous techniques for machine learning and deep learning, with a particular focus on COVID-19 pneumonia. However, most of these techniques have raised concerns with regard to diagnostic precision as a result of the limited application of advanced algorithms, datasets whose validation is mostly inadequate and predictive capability. To address these limitations, our research introduces a deep learning-based approach by Convolutional Neural Networks (CNNs), which enhances the performance in classifying COVID-19 pneumonia. Salient features of the proposed method include a four-step process: first, data acquisition from a comprehensive chest X-ray dataset on GitHub; second, processing and analyzing the data through visual means like histograms and scatter plots; third, using CNNs supplemented with techniques for data augmentation in supervised learning; lastly, performance evaluation to benchmark against existing models. The present study uses features from X-ray images with the help of CNN's advanced pattern recognition capabilities in pursuit of achieving better generalization and precision in classification. The model achieved an accuracy of 85.70\% and precision of 88.6%, which surpasses the existing techniques and thereby built the promise of improving the accuracy of the diagnostic process, hence, leading to improved care for the patients, and more so forms the foundation on which future AI-powered healthcare diagnostics are expected to take off.

Handover efficiency in wireless and mobile communication networks is an important factor in providing QoS guarantees for real time applications. The 802.11 standard requires a Mobile Node (MN) to scan all the possible channels (11 in the US) to discover available Access Points (AP's) during a handover. In a given 802.11 cell several channels are expected to be empty. Due to a full scan the MN ends up wasting time scanning empty channels, which results in a high handover delay. This handover delay can be reduced by simple refining the scanning procedure to a limited set of channels, currently being used throughout the network or by the surrounding access points. In this paper we propose to use the Media Independent Information Server (MIIS) to inform the MN of the channel configuration information of the network or the surrounding AP's. In our scheme the MN acquires channel configuration information from MIIS server and then uses that information to scan a limited set of channels being used by the surrounding access points or through out the network rather than scanning all possible channels resulting in reduced handover delay. We define a parameter NET_CHANNEL_CONFIG which is passed in the query by the MN to the MIIS, so that the MIIS knows which information is being requested by the MN. We also define an Information Element Container IE_CONTAINER_802.11_CHINFO which is used by MIIS to return the requested channel configuration information of the network to the MN. We call our scheme Intelligent Scan.

Network selection in 4G networks by nature is a multi-dimensional problem and many parameters need to be considered for handover decision making. Successful classical handover methods based on Received Signal Strength (RSS) are no longer applicable for heterogeneous handovers because different network standards use different RSS and cannot be compared directly to each other. Heterogeneity does not only exist in network architectures and in their respective Quality of Service (QoS) but also in user application needs and user contexts. Network selection in heterogeneous networks has been modeled as Multiple Attribute Decision Making (MADM). In this paper we investigate how the task of network selection can be distributed across multiple network entities putting minimum possible burden on the Mobile Node (MN) which usually is the most restricted network component in terms of memory, processing power and battery life. This distribution increases the scalability of handover algorithm and will also allow the inclusion of more parameters for handover decision making. Considering more parameters for handovers will result in accurate, robust and general algorithms, capable of fulfilling a variety of user contextual, preferential and applications QoS needs. The Media Independent Handover (MIH) and its different services are used to retrieve and share information among MIH enabled nodes and for conformity among different network standards. Simulations are used to show the effectiveness of a simple handover algorithm based on Simple Additive Weighting (SAW) for network selection considering different types of user services and Service Level Agreement (SLA's). SAW belongs to the MADM classification of heterogeneous handovers and is chosen here due its simplicity and efficiency. The handover efficiency of the proposed distributed schemes is compared with an ”802.11 Preferred” scheme. Only handovers between Wi-Fi and WiMAX are considered. But the handover framework is general and can be extended to consider other wireless and mobile communication networks like 3G, CDMA etc.

The Google Play Store, the primary distribution platform for Android applications, hosts millions of apps and receives a large number of user reviews. However, extracting actionable insights from these reviews, particularly classifying rating sentiment (positive, negative, or neutral), remains a challenge. This paper addresses this issue by proposing a novel framework for app rating sentiment classification on Google Play Store data. We leverage a rich dataset of app reviews acquired from GitHub and employ a battery of advanced machine learning models. Specifically, we explore the efficacy of AdaBoost, XGBoost, and Artificial Neural Networks (ANNs) in conjunction with optimization techniques. Our approach significantly outperforms existing research, achieving superior accuracy ranging from 85% to 98% compared to the 78-95.9% accuracy reported in prior studies. This significant improvement translates to a deeper understanding of user sentiment across the app ecosystem. It enables developers to better gauge user satisfaction, prioritize improvements, and ultimately enhance user experience. Our work also paves the way for further research in sentiment analysis of app reviews, exploring fine-grained sentiment detection and analyzing sentiment dynamics over time.