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.

In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.

Tuberculosis (TB) is an infectious disease primarily impacting the lungs. It spreads through the air when an infected person coughs, sneezes, or talks. Diagnosing TB involves clinical examinations and specialized tests performed by medical professionals. Coughing is a common symptom. The diagnosis of TB involves clinical examinations and specialized tests. However, studies have shown that medical doctors can distinguish between cough sounds associated with different respiratory conditions. Therefore, using artificial intelligence to analyze cough recordings of patients to diagnose TB is a promising research direction. In this study, we propose a customized cascaded approach for diagnosing TB using cough audio. This approach involves a series of models arranged in a sequence, where the output of one model serves as the input for the next. In the first phase, we distinguish between bursts in audio signal as noise or cough. In the second phase, we classify cough as TB or non-TB. Non-TB cough includes both voluntary and non-TB reflex cough. For this study we collected a dataset consisting of cough audio recordings from TB and non-TB patients at Mayo Hospital in Lahore, Pakistan. The recordings were obtained using the AI4LYF DCT application, a fully automated phone-based system, with no manual annotation. We apply statistical classifiers based on spectral and time domain features, both with and without clinical metadata. Through a stratified grouped cross-validation approach, our results show that using cough sounds along with demographic and clinical factors yielded an accuracy of 97% when the random forest was used. Similarly, for all other classifiers, the accuracies were ≥ 90% when demographic and clinical data was included (from ≤ 80ˆ). Our findings suggest that our model based on patient data and cough auido could support community health workers and health programs in identifying TB cases more effectively and cost-efficiently.

Effective management of information technology infrastructure (ITI) is key to success for any enterprise offering IT services in this contemporary digital era. Considering the importance of the topic both for research and practice, a Systematic Mapping Study (SMS) was conducted (with the following research questions) to present state-of-the-art concerning the management of ITI, (i) what focus areas related to the management of IT Infrastructure have been considered in the extant literature?, (ii) what are the publication trends and characteristics of existing research on IT Infrastructure Management?, (iii) what are the main challenges in the context of managing IT Infrastructure reported in the existing literature?, and (iv) what recommendations and guidelines have been proposed to overcome the reported challenges? Following the systematic approach, n=57 articles from extant literature satisfying this study’s inclusion and exclusion criteria were considered for analysis to find answers to these research questions. The findings reveal (i) research focus areas categorized into 6 categories (i.e., resource, service, learning style, self-care, data sharing & access, and human ITI capability), (ii) a set of variables to map publication trends and characteristics of existing research with “ evaluation research ”, “ constructive research ” and “ models ” recognized as the most common paper type, research methods, and contribution type respectively, (iii) 28 challenges related to ITI management in enterprises were identified, and (iv) 68 guidelines and recommendations for organizations and personnel were identified to manage the ITI more efficiently. The findings such as research focus areas and publication trends are important from a research perspective offering guidance to researchers to drive further research on the topic. Meanwhile, the challenges, recommendations, and guidelines have practical implications, providing practitioners with insights into better understanding and management of their respective ITI.

Rice is a staple food crop cultivated worldwide and is essential to the livelihoods of millions. Among the common threats to rice cultivation, leaf diseases are particularly devastating, leading to reduced yields and significant financial losses. Traditional detection methods, relying on manual field inspections, are often tedious, time-consuming, and require specialized expertise. In recent years, automated rice disease detection technologies have seen significant advancements. This paper presents a review of emerging techniques for rice disease detection, categorizing prior studies based on artificial intelligence (AI), image processing (IP), and the Internet of Things (IoT). It also examines the types of rice diseases, available datasets, hyperparameter optimization strategies, and their influence on model performance highlighting the importance of fine-tuning. The review is based on 125 high-quality publications and identifies promising future research directions by addressing existing gaps at the intersection of AI, IP, IoT, and rice disease detection.