Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time‐consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet‐based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet‐5‐based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet‐5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.

Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble‐learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD‐UFES‐20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous study for the PAD‐UFES‐20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. In conclusion, the study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging.

Precision agriculture is now essential in today’s world, especially for countries with limited water resources, fertile land, and enormous population. Smart irrigation systems can help countries efficiently utilize fresh water and use the excess water for barren lands. Smart water management platform (SWAMP) is an IoT-based smart irrigation project designed for efficient freshwater utilization in agriculture. The primary aim of SWAMP is to auto manage water reserves, distribution, and consumption of various levels, avoid over-irrigation and under-irrigation problems, and auto manage time to maximize production. This research proposed an energy-efficient water management platform (EEWMP), an improved version of SWAMP. EEWMP is an IoT-based smart irrigation system that uses field-deployed sensors, sinks, fusion centres, and open-source clouds. Both models’ performance is evaluated in energy consumption, network stability period, packet sent to destination, and packet delivery ratio. The experimental results show that EEWMP consumes 30% less energy and increases network stability twice than SWAMP. EEWMP can be used in different irrigation models such as drip irrigation, sprinkler irrigation, surface irrigation, and lateral move irrigation with subtle alterations. Moreover, it can also be used in small farms of third-world countries with their existing communication infrastructures such as 2G or 3G.

The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD (Urdu Slang and Abusive words Detection), a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models.

Sensor fault classification and reconstruction frameworks are crucial for the stable, safe, and reliable operations of Structural Health Monitoring (SHM) systems. Existing literature addressing reliability and efficiency is confronted with several challenges; especially, lacking a combined framework addressing both issues of classification and reconstruction at the same time. To tackle these issues, this paper proposes a fault-tolerant mechanism that uses various combinations of Deep Learning (DL) techniques to ensure the effectiveness and reliability of SHM systems in a resource-efficient way. The proposed mechanism is an integrated framework consisting of two modules: the sensor faults classification module and the faulty signal reconstruction module. We develop integrated architectures of CNN and RNN to classify faulty signals and employ various architectures of LSTM models for faulty signal reconstruction. Both modules are tested on the benchmark Canton Tower dataset. We augment the dataset with faulty signals created through simulations for an accurate analysis. The sensor faults classification module is evaluated by utilizing precision, recall, F1-score, and accuracy; it achieves a maximum accuracy of 94%. Additionally, the root mean square error (RMSE) value for the faulty signals’ reconstruction stands at zero. The experimental results show that our proposed mechanism outperforms existing state-of-the-art techniques regarding sensor fault classification accuracy and the quality of reconstructed faulty signals.