Agricultural food production is projected to be 70% higher by 2050 than it is today, with the world population rising to more than 9 billion, 34% higher than it is now. The farmers have been forced to produce more with the same resources. This pressure means that optimizing productivity is one of the main objectives of the producers but also in a sustainable way. Not only does agriculture face a decline in production, but it has also had to face limitations in data collection, storing, securing, and sharing, climate change, increases in input prices, traditional food supply chain systems where there is no direct connection between the farmer and the buyer, and limitations on energy use. Existing IoT‐based agriculture systems have a centralized format and operate in isolation, leaving room for unresolved issues and major concerns, including data security, manipulation, and single failure points. This paper proposes a futuristic IoT with a blockchain model to meet these challenges. Further, this paper also proposes and novel energy‐efficient clustering IoT‐based agriculture protocol for lower energy consumption and network stability and compares its results with its counterpart low‐energy adoptive clustering hierarchy (LEACH) protocol. The simulation results show that the proposed protocol network stability is 23% higher as compared to LEACH as first node of LEACH dies at 168 rounds while IoT‐based agriculture first node dies after 463 rounds. Similarly, IoT‐based agriculture protocol energy consumption is 68% lower than that of LEACH. The proposed protocol also extends the network life to more rounds and demonstrates an increase of 112%.

Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32 average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99 average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88 average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.

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.

Modern agriculture faces critical challenges, including climate change induced water shortage, inefficient resource utilization, and reducing soil health which reduce sustainability and reduce crop yields. Traditional farming methods, dependent on manual observation and generalized practices, often lack the precision needed to address these growing issues. To meet these demands, this study presents an artificial intelligence (AI) powered smart agriculture system that integrates deep learning (DL), Internet of Things (IoT), and explainable artificial intelligence (XAI) to enable intelligent, data-driven farming. This study addresses key challenges such as ineffective soil management, poor irrigation practices, and the absence of personalized crop and fertilizer recommendations. IoT-based sensors provide real-time soil analysis, measuring critical parameters such as nutrient levels, moisture, and temperature. Advanced DL models were employed for various tasks in the AI-Driven Smart Agriculture System. The Transformer-based Tabular Learning (TTL) model for irrigation advice gives an accuracy of 99.13%. For soil analysis, the TabNet regressor achieved a root mean square error (RMSE) of 1.51 and an R² score of 98.7%, while XAI techniques were used to explain and interpret its predictions. The Sparse Weighted Fusion Transformer (SwiFT) model for crop recommendation gives an accuracy of 98.75%. For fertilizer recommendation, the TabNet classifier achieved an accuracy of 99.3%, with XAI techniques applied to explain the reasoning behind its predictions. These results demonstrate that the system delivers precise and actionable insights. A weather Application Programming Interface (API) is integrated to support timely irrigation decisions through accurate forecasting. A chatbot interface is also added which enhances system usability, accessibility and user-friendliness for farmers. The proposed model improves crop productivity, optimizes resource use, and supports adaptation to environmental changes. By leveraging AI and IoT technologies, the proposed model gives a solution which helps in sustainable agriculture, environmental resilience, and global food security, thus benefiting stakeholders ranging from individual farmers to large-scale agricultural business. Future work will explore real-time adaptive control mechanisms and expand model generalization across diverse geographical region.

Over the last two decades, social media platforms have grown dramatically. Twitter and Facebook are the two most popular social media platforms, with millions of active users posting billions of messages daily. These platforms allow users to have freedom of expression. However, some users exploit this facility by disseminating hate speeches. Manual detection and censorship of such hate speeches are impractical; thus, an automatic detection mechanism is required to detect and counter hate speeches in a real-time environment. Most research in hate speech detection has been carried out in the English language. Still, minimal work has been explored in other languages, mainly Urdu written in Roman Urdu script. A few research have attempted machine learning, and deep learning models for Roman Urdu hate speech detection; however, due to a scarcity of Roman Urdu resources, and a large corpus with defined annotation rules, a robust hate speech detection model is still required. With this motivation, this study contributes in the following manner: we developed annotation guidelines for Roman Urdu Hate Speech. Second, we constructed a new Roman Urdu Hate Speech Dataset (RU-HSD-30K) that was annotated by a team of experts using the annotation rules. To the best of our knowledge, the Bi-LSTM model with an attention layer for Roman-Urdu Hate Speech Detection has not been explored. Therefore, we developed a context-aware Roman Urdu Hate Speech detection model based on Bi-LSTM with an attention layer and used custom word2vec for word embeddings. Finally, we examined the effect of lexical normalization of Roman Urdu words on the performance of the proposed model. Different traditional as well as deep learning models, including LSTM and CNN models, were used as baseline models. The performance of the models was assessed in terms of evaluation matrices like accuracy, precision, recall, and F1-score. The generalization of each model is also evaluated on a cross-domain dataset. Experimental results revealed that Bi-LSTM with attention outperformed the traditional machine learning models and other deep learning models with an accuracy score of 0.875 and an F-Score of 0.885. In addition, the results demonstrated that our suggested model (Bi-LSTM with Attention Layer) is more general than previous models when applied to unseen data. The results confirmed that lexical normalization of Roman Urdu words enhanced the performance of the suggested model.