Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

When neural networks are trained incrementally, inputoutput relationships that are trained formerly tend to be collapsed by the learning of new training data. This phenomenon is called "interference". To suppress the interference, we have proposed an incremental learning system (

The evolving "Industry 4.0" domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs. Federated learning (FL) becomes a recent advancement of machine learning (ML) which aims to protect user data. In this aspect, this study designs federated learning with blockchain assisted image classification model for clustered UAV networks (FLBIC-CUAV) on IIoT environment. The proposed FLBIC-CUAV technique involves three major processes namely clustering, blockchain enabled secure communication and FL based image classification. For UAV cluster construction process, beetle swarm optimization (BSO) algorithm with three input parameters is designed to cluster the UAVs for effective communication. In addition, blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers. Finally, the cloud server uses an FL with Residual Network model to carry out the image classification process. A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach. The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.

Remote sensing image (RSI) scene classification has become a hot research topic due to its applicability in different domains such as object recognition, land use classification, image retrieval, and surveillance. During RSI classification process, a class label will be allocated to every scene class based on the semantic details, which is significant in real-time applications such as mineral exploration, forestry, vegetation, weather, and oceanography. Deep learning (DL) approaches, particularly the convolutional neural network (CNN), have shown enhanced outcomes on the RSI classification process owing to the significant aspect of feature learning as well as reasoning. In this aspect, this study develops fuzzy cognitive maps with a bird swarm optimization-based RSI classification (FCMBS-RSIC) model. The proposed FCMBS-RSIC technique inherits the advantages of fuzzy logic (FL) and swarms intelligence (SI) concepts. In order to transform the RSI into a compatible format, preprocessing is carried out. Besides, the features are produced by the use of the RetinaNet model. Besides, a FCM-based classifier is involved to allocate proper class labels to the RSIs and the classification performance can be improved by the design of bird swarm algorithm (BSA). The performance validation of the FCMBS-RSIC technique takes place using benchmark open access datasets, and the experimental results reported the enhanced outcomes of the FCMBS-RSIC technique over its state-of-the-art approaches.

Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases. With this motivation, this paper introduces a novel AI enabled apple leaf disease classification (AIE-ALDC) technique for precision agriculture. The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes. In addition, the AIE-ALDC technique includes a Capsule Network (CapsNet) based feature extractor to generate a helpful set of feature vectors. Moreover, water wave optimization (WWO) technique is employed as a hyperparameter optimizer of the CapsNet model. Finally, bidirectional long short term memory (BiLSTM) model is used as a classifier to determine the appropriate class labels of the apple leaf images. The design of AIE-ALDC technique incorporating the WWO based CapsNet model with BiLSTM classifier shows the novelty of the work. A wide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique. The experimental results demonstrate the promising performance of the AIE-ALDC technique over the recent state of art methods.

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

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