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To prevent vulnerabilities and ensure app security, smart contract vulnerability detection identifies flaws in blockchain code. To overcome the limitations of traditional detection methods, this study introduces a novel approach that combines Explainable Artificial Intelligence (XAI) with Deep Learning (DL) to detect vulnerabilities in smart contracts. The proposed intellectual engine operates in multiple stages. First, a smart contract is created, and the user provides a value during the runtime phase. XAI and DL then analyze the opcodes in high-value contracts to detect potentially risky processes. If violations such as security protocol failures, insufficient funds, or account restrictions are found, the engine halts the transaction and generates an error report. If the contract passes this vulnerability assessment, it continues executing without interruption. This ensures flagged transactions remain functional while being assessed. Our proposed Hybrid Boot Branch and Bound Long Short-Term Memory (HB 3 LSTM) approach achieves outstanding performance, with an accuracy of 99.68%, precision of 99.43%, recall of 99.54%, and an F1-score of 99.40%, which surpasses the performance of existing methods.

Digital advancements have made cloud computing and IoT essential for innovative environments such as healthcare and industry. Cloud platforms offer scalable compute and storage capabilities, whereas IoT devices generate real‐time data. However, there are significant challenges faced while integrating the IoT with cloud to achieve robust, scalable, and secure access control. Traditional centralized models, such as static rule‐based mechanisms and public key infrastructure (PKI), are prone to single points of failure and suffer from limited scalability and poor adaptability. To address these issues, this paper proposes a decentralized access control architecture that combines blockchain with a hybrid bidirectional graph convolutional network (Bi‐GCN). The framework integrates ciphertext policy‐attribute based encryption (CP‐ABE) with trusted platform module (TPM)–based pseudonymous identities and the blockchain smart contracts for fine‐ and hardware‐assisted access control. A generative adversarial network (GAN)‐assisted prevalidation layer filters sybil, tampering, and spoofing attempts before block inclusion, enhancing integrity and reducing overhead. Bi‐GCN supports real‐time anomaly detection, trust adaptation, and behavior profiling, while smart contracts enforce adaptive role‐attribute policies. Experimental results show that the proposed model outperforms existing methods across key metrics, including 0.97 accuracy, 0.98 F‐measure, and minimal security overhead of 0.7%. Although it introduces slight latency due to advanced processing, the benefits of secure and intelligent access management outweigh the trade‐off. The integration of blockchain ensures decentralized and immutable policy enforcement, while Bi‐GCN facilitates self‐adaptive security, making the architecture suitable for dynamic IoT–cloud ecosystems.

Schizophrenia is a chronic mental disorder that impairs a person’s thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R 2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.

The identification and classification of plant diseases in the crop is an important aspect of the agriculture sector. The transfer learning approach in the deep learning model of the Alexnet architecture with the Adaptive Moment Estimation (ADAM) optimizer is used for training which combines the advantages of RMSprop and Stochastic Gradient Descent with the momentum (SGDM) algorithm. The 25-layer Alexnet model with 5 convolutional layers, 3 max-pooling layers, 2 normalization layers, 2 fully connected layers, and 1 softmax layer is considered for classifying 5300 images divided into four categories: healthy, blight, common rust, and gray leaf spot. Various hyperparameter settings such as learning rates, number of epochs, training–testing ratio, and different optimizers such as ADAM, SGDM, and RMSprop are tuned to train the proposed model. The experimental results for various learning rates are presented. A comparison with other existing approaches revealed that our proposed approach produced an accuracy of 99.43% for an optimal learning rate of 0.0001.This proposed work will find application in many agricultural sectors for implementation of the automated systems like robotic pesticide sprayers and drone operated systems.

Iron deficiency anemia (IDA) is an important public health issue in India. This study was performed to determine the impact of acupuncture at the GB39, BL17, and LR13 points on hemoglobin levels, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and red cell distribution width (RDW) in people with IDA. One hundred women with IDA were randomly allocated to the acupuncture group (AG) or placebo control group (PCG). For 30 minutes per day, daily for 2 weeks, the AG received acupuncture at GB39, BL17, and LR13, while the PCG received needling at non-acupuncture points. Outcomes were assessed before and after the intervention. We found a significant increase ( p < 0.001) in hemoglobin level (AG 10.39–11.38 g/dl, effect size 0.785; PCG 10.58–10.40 g/dl, effect size 0.191), MCH (AG 25.69–27.50 fl, effect size 0.418; PCG 27.43–27.23 fl, effect size 0.058), and RDW (AG 15.12–16.41 fl, effect size 0.626; PCG 14.91–14.94 fl, effect size 0.017) in the AG compared to the PCG. Results suggest that needling at the GB39, BL17, and LR13 acupuncture points is more effective in treating people with IDA than needling at non-acupuncture points.

Artificial Intelligence (AI) when incorporated with machine learning and deep learning algorithms can have a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, it covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision support applications, includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers and students in the various domains of computer science engineering, electronics and communication engineering, information technology, industrial engineers, biomedical engineers, and management will find this book useful. By the end of this book, you will have understood the fundamentals of AI and various case studies that will develop your adaptive thinking to solve real time AI problems.

In big data, analysis data is collected from different sources in various formats, transforming into the aspect of cleansing the data, customization, and loading it into a Data Warehouse. Extracting data in other formats and transforming it to the required format requires transformation algorithms. This transformation stage has redundancy issues and is stored across any location in the data warehouse, which increases computation costs. The main issues in big data ETL are handling high-dimensional data and maintaining similar data for effective data warehouse usage. Therefore, Extract, Transform, Load (ETL) plays a vital role in extracting meaningful information from the data warehouse and trying to retain the users. This paper proposes hybrid optimization of Swarm Intelligence with a tabu search algorithm for handling big data in a cloud-based architecture-based ETL process. This proposed work overcomes many issues related to complex data storage and retrieval in the data warehouse. Swarm Intelligence algorithms can overcome problems like high dimensional data, dynamical change of huge data and cost optimization in the transformation stage. In this work for the swarm intelligence algorithm, a Grey-Wolf Optimizer (GWO) is implemented to reduce the high dimensionality of data. Tabu Search (TS) is used for clustering the relevant data as a group. Clustering means the segregation of relevant data accurately from the data warehouse. The cluster size in the ETL process can be optimized by the proposed work of (GWO-TS). Therefore, the huge data in the warehouse can be processed within an expected latency.

Classification of plant diseases is an important aspect of agriculture and this proposed methodology aims at identification, prediction and classification of corn leaf disease using AlexNet architecture with transfer learning methodology and AlexNet-Inception model. Different optimizers such as Stochastic Gradient Descent with Momentum, RMSprop and ADAM were employed in training the network. A 25-layer AlexNet model with transfer learning approach was modelled to sort the dataset into 4 classes, healthy, blight, common rust, and grey leaf spot. Both the networks were trained with multiple hyper parameter configurations with various learning rates, mini batch sizes, and training-to-test ratios. The modified AlexNet-Inception network performs multiple parallel convolution operations with different sizes of filters 1 × 1, 3 × 3 and 5 × 5 and average pooling on the output of 2D max pooling layer of AlexNet layer and these outputs are concatenated to produce one output. Thus the network gets progressively wider, not deeper and the computational cost is reduced and thereby avoiding the vanishing gradient problem. The detailed analysis of the features that were prioritized by AlexNet and AlexNet-Inception network for the classification of test images were validated with LIME and Grad-CAM technique and it was proved that AlexNet-Inception network outperforms the AlexNet transfer learning approach and the accuracy achieved were 98.91% for the ideal learning rate setting of 0.0001. The trials’ findings indicate that the algorithm is more precise and quicker than conventional AlexNet model, providing a novel method for detecting abnormalities in maize plants. In a number of agricultural industries, this suggested effort can be utilized to implement in real time applications.

[This corrects the article DOI: 10.1007/s11571-022-09817-y.].

[...]Ayurvedic medicine represents an ancient healthcare tradition that is becoming increasingly popular as an alternative medicine modality. Neurotrophic potencies of the immunophilin ligands resemble their potencies in binding to and inhibiting the rotamase activity of FKBP-12 or cyclophilin. Since nonimmunosuppressive immunophilin ligands, which are devoid of calcineurin inhibitory activity, are equally neurotrophic, inhibition of calcineurin activity is not the mediator of the neurotrophic effects. According to the latest research, people with high levels of markers for vitamin B12 deficiency were more likely to score lower on cognitive tests, as well as have a smaller total brain volume, [31] which suggests a lack of the vitamin may contribute to brain shrinkage. Examples of such plasticity include blood cells becoming neurons, liver cells that can be made to produce insulin, and hematopoietic stem cells that can develop into heart muscle. [...]exploring the possibility of using adult stem cells for cell-based therapies has become a very active research area.