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Abstract Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements. These approaches make certain assumptions regarding false data injection (FDI) attacks, which are inadequate and restrictive to cope with cyberattacks. The reliance on communication technology has emphasized the exposure of power systems to FDI assaults that can bypass the current bad data detection (BDD) mechanism. The current study on unobservable FDI attacks (FDIA) reveals the severe threat of secured system operation because these attacks can avoid the BDD method. Thus, a Data-driven learning-based approach helps detect unobservable FDIAs in distribution systems to mitigate these risks. This study presents a new Hybrid Metaheuristics-based Dimensionality Reduction with Deep Learning for FDIA (HMDR-DLFDIA) Detection technique for Enhanced Network Security. The primary objective of the HMDR-DLFDIA technique is to recognize and classify FDIA attacks in the distribution systems. In the HMDR-DLFDIA technique, the min–max scalar is primarily used for the data normalization process. Besides, a hybrid Harris Hawks optimizer with a sine cosine algorithm (hybrid HHO‐SCA) is applied for feature selection. For FDIA detection, the HMDR-DLFDIA technique utilizes the stacked autoencoder (SAE) method. To improve the detection outcomes of the SAE model, the gazelle optimization algorithm (GOA) is exploited. A complete set of experiments was organized to highlight the supremacy of the HMDR-DLFDIA method. The comprehensive result analysis stated that the HMDR-DLFDIA technique performed better than existing DL models.

Bone cancer detection is an essential region of medical analysis but developments in medical imaging and artificial intelligence (AI) are vital. Using approaches, namely deep learning (DL) and machine learning (ML), radiologists and medical staff can examine X-ray, CT, and MRI scans to identify bone cancer and abnormalities. These technologies support earlier diagnosis, correct diagnosis, and treatment planning, enhancing patient solutions. The combination of AI-driven image analysis and the knowledge of medical practitioners improves the speed and precision of bone cancer detection, contributing to more effectual clinical activities. DL algorithms, particularly CNNs, are exposed to great performance in image classification tasks and are extremely utilized for medical image analysis. We offer a Hybrid Rice Optimization Algorithm with DL-Assisted Bone Cancer Detection (HROADL-BCD) technique on medical X-ray images. The major intention of the HROADL-BCD method is to examine the X-ray images for the recognition of bone cancer. In the presented HROADL-BCD method, a bilateral filtering (BF) process was performed to remove the noise. To derive feature vectors, the HROADL-BCD technique applied the EfficientNet model. The HROADL-BCD technique involved the HROA for hyperparameter tuning of the EfficientNet model. Last, the bone cancer detection and classification process were executed by the attention-based bidirectional long short-term memory (ABiLSTM) approach. A wide range of simulations could be applied for the simulation result analysis of the HROADL-BCD algorithm. The extensive outcome of the HROADL-BCD approach inferred the superior outcome of 97.62% outcome concerning various aspects.

Vehicle detection in Remote Sensing Images (RSI) is a specific application of object recognition like satellite or aerial imagery. This application is highly beneficial in different fields like defense, traffic monitoring, and urban planning. However, complex particulars about the vehicles and the surrounding background, delivered by the RSIs, need sophisticated investigation techniques depending on large data models. This is crucial though the amount of reliable and labelled training datasets is still a constraint. The challenges involved in vehicle detection from the RSIs include variations in vehicle orientations, appearances, and sizes due to dissimilar imaging conditions, weather, and terrain. Both specific architecture and hyperparameters of the Deep Learning (DL) algorithm must be tailored to the features of RS data and the nature of vehicle detection tasks. Therefore, the current study proposes the Intelligent Water Drop Algorithm with Deep Learning-Driven Vehicle Detection and Classification (IWDADL-VDC) methodology to be applied upon the Remote Sensing Images. The IWDADL-VDC technique exploits a hyperparameter-tuned DL model for both recognition and classification of the vehicles. In order to accomplish this, the IWDADL-VDC technique follows two major stages, namely vehicle detection and classification. For vehicle detection process, the IWDADL-VDC method uses the improved YOLO-v7 model. After the vehicles are detected, the next stage of classification is performed with the help of Deep Long Short-Term Memory (DLSTM) approach. In order to enhance the classification outcomes of the DLSTM model, the IWDA-based hyperparameter tuning process has been employed in this study. The experimental validation of the model was conducted using a benchmark dataset and the results attained by the IWDADL-VDC technique were promising over other recent approaches.

Chronic diseases, such as diabetes, cause serious challenges worldwide due to their long-lasting effect on health and quality of life. Diabetes, considered a high glucose level, requires continuous management to avoid complications like kidney failure, low vision, and heart disease. Leveraging state-of-the-art technology, especially IoT devices, shows great potential for optimizing the recognition and management of diabetes. These devices, including smart glucose monitors and wearable sensors, provide real-time data on crucial health metrics, enabling early intervention and proactive monitoring. Furthermore, incorporating deep learning (DL) techniques improves data analysis and recognizes risk factors and subtle patterns related to diabetes. By integrating IoT technology with DL techniques, the healthcare system can empower patients with tools for self-management and develop more conventional approaches for earlier diagnosis and personalized treatment plans, ultimately improving long-term health outcomes and reducing the burden of diabetes. This study develops a new IoT-enabled Driven Early Detection of Chronic Disease using the DL and Dimensionality Reduction (EDCD-DLDR) approach. The EDCD-DLDR method aims to enable IoT devices to collect patient medical data and employ the DL model for earlier diagnosis of diabetes. In the EDCD-DLDR technique, the IoT-based data acquisition process is initially involved, and the collected data gets normalized using Z-score normalization. The EDCD-DLDR technique uses an artificial rabbit optimizer-based feature selection (ARO-FS) approach for dimensionality reduction. In addition, the detection of diabetes is achieved by the attention bidirectional gated recurrent unit (ABiGRU) model. The pelican optimization algorithm (POA) based hyperparameter selection model is included to improve the performance of the ABiGRU network. A comprehensive set of simulations is made to highlight the performance of the EDCD-DLDR method on the Kaggle dataset. The experimental validation of the EDCD-DLDR method underscored a superior accuracy value of 97.14% over existing techniques.

Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. These models influence features extracted from medical imaging, genetic, and clinical data to distinguish between benign and malignant tumours, define cancer stages, and guide treatment verdicts. By automating cancer detection and classification procedures, DL techniques help healthcare experts make quicker and more exact analyses, leading to superior patient results, modified treatment tactics, and enhanced complete organization of GC cases. This technique has great promise for transforming initial detection and involvement in the battle against dangerous diseases. With this inspiration, this study presents a new snake optimization algorithm with a DL-assisted GC classification (SOADL-GCC) approach. The SOADL-GCC approach aims to examine the gastrointestinal tract images for the detection and classification of GC. To achieve it, the SOADL-GCC model employs a bilateral filtering (BF) approach for the noise removal process and enhances image quality. Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. Moreover, SOA can achieve the optimum assortment of hyperparameters associated with the CapsNet model. Finally, the classification process can be performed using the deep belief network (DBN) model. A sequence of simulations took place on the Kvasir dataset to evaluate improved detection results of SOADL-GCC technology. An extensive comparative study reported that the SOADL-GCC technique effectively performs well with other models with a maximum accuracy of 99.72%.

The journey towards Industry 4.0 have driven the workforce with the need for continuous competency development to stay on top of their profession and remain attractive to their employers. In this context, E-Portfolio has been conceived as a promising tool for both professionals and employers, in assessing and providing guidance for further workplace learning. Nevertheless, the challenges in implementing E-Portfolio are daunting, particularly with regard to lack of interoperability while collecting and sharing the competency evidences across multiple organizations and institutions. The increasing move towards professional (learner)-centered interoperability poses additional challenges that needs to be addressed to realize the full potential of E-Portfolio in the context of Industry 4.0. This work puts emphasize on realization of professional-centered interoperability and proposes a framework leveraging the advantage of blockchain, decentralized storage system interplanetary file system, smart contract and session management for E-Portfolio creation and sharing evidences across multiple organizations without comprising the confidentiality, integrity and availability of the shared evidences. The proof of concept simulation on Ethereum official test network Rinkeby demonstrates the feasibility of proposed framework in practice. In addition, the evaluation of the proposed framework demonstrates its effectiveness from the aspects of cost and security as a potential solution for implementing E-Portfolio and helping the professional to remain competitive in global labor market.

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.

Brain tumour (BT) detection involves the process of identifying the presence of a brain tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging techniques, such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), or PET (Positron Emission Tomography) scans. Early detection of BT is important and MRI is one of the primary imaging techniques used to diagnose and assess BT. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs) have shown promising results in assisting with BT detection on MRI scans. This study designs an Ensemble Learning Driven Computer-Aided Diagnosis Model for Brain Tumor Classification (ELCAD-BTC) technique on MRIs. The presented system purposes to detect and classify various steps of BTs. The presented system contains a Gabor filtering (GF) approach to remove the noise and increase the quality of MRI images. Moreover, ensemble learning of three DL models namely EfficientNet, DenseNet, and MobileNet is utilized as feature extractors. Furthermore, the denoising autoencoder (DAE) approach can be exploited to detect the presence of BTs. Finally, a social spider optimization algorithm (SSOA) was carried out for the hyperparameter tuning of the DL models. For simulating the improved BT classification outcome, a brief set of simulations occur on BRATS 2015 database.

The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.

Recently, the techniques of Internet of Things (IoT) and mobile communications have been developed to gather human and environment information data for a variety of intelligent services and applications. Remote monitoring of elderly and disabled people living in smart homes is highly challenging due to probable accidents which might occur due to daily activities such as falls. For elderly people, fall is considered as a major reason for death of post-traumatic complication. So, early identification of elderly people falls in smart homes is needed to increase the survival rate of the person or offer required support. Recently, the advent of artificial intelligence (AI), IoT, wearables, smartphones, etc. makes it feasible to design fall detection systems for smart homecare. In this view, this paper presents an IoT enabled elderly fall detection model using optimal deep convolutional neural network (IMEFD-ODCNN) for smart homecare. The goal of the IMEFD-ODCNN model is to enable smartphones and intelligent deep learning (DL) algorithms to detect the occurrence of falls in the smart home. Primarily, the input video captured by the IoT devices is pre-processed in different ways like resizing, augmentation, and min-max based normalization. Besides, SqueezeNet model is employed as a feature extraction technique to derive appropriate feature vectors for fall detection. In addition, the hyperparameter tuning of the SqueezeNet model takes place using the salp swarm optimization (SSO) algorithm. Finally, sparrow search optimization algorithm (SSOA) with variational autoencoder (VAE), called SSOA-VAE based classifier is employed for the classification of fall and non-fall events. Finally, in case of fall event detected, the smartphone sends an alert to the caretakers and hospital management. The performance validation of the IMEFD-ODCNN model takes place on UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the promising performance of the IMEFD-ODCNN model over the recent methods with the maximum accuracy of 99.76% and 99.57% on the multiple cameras fall and UR fall detection dataset.

Presently, sensor-cloud based environment becomes highly beneficial due to its applicability in several domains. Wireless multimedia sensor network (WMSN) is one among them, which involves a set of multimedia sensors to collect data about the deployed region. Compared to traditional object tracking models, animal tracking in WMSN is a tedious process owing to the harsh, dynamic, and energy limited sensors. This article introduces a new Reliable Multi-Object Tracking Model using Deep Learning (DL) and Energy Efficient WMSN. Initially, the fuzzy logic technique is employed to determine the cluster heads (CHs) to attain energy efficiency. Next, in the second stage, a novel tracking algorithm by the use of Recurrent Neural Network (RNN) with a tumbling effect called RNN-T is developed. The proposed RNN-T model gets executed by every sensor node and the CHs execute the tracking algorithm to track the animals. Finally, the tracking results are transmitted to the cloud server for investigation purposes. In order to assess the performance of the presented model, an extensive experimental analysis is carried out by the use of a real-time wildlife video. The obtained results ensured that the RNN-T model has achieved better performance over the compared methods in different aspects.

Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures.

The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.

Image segmentation plays crucial role in medical image analysis and forms the basis for clinical diagnosis and patient's treatment planning. But the large variation in organ shapes, inhomogeneous intensities, poor contrast, organic nature of textures and complex boundaries in medical images makes segmentation process adverse and challenging. Further, the absence of annotated ground-truth dataset in medical field limits the advantages of the trending deep learning techniques causing several setbacks. Though numerous unsupervised methods are reported in literature to combat the challenges in medical domain, achieving better segmentation quality still remains as an open issue. This work aim to address this issue integrating the strength of multiresolution analysis and the meta-heuristic optimization techniques for unsupervised medical image segmentation. The proposed approach employs undecimated wavelet frames to extract translation invariant texture and gray information at different orientations to effectively characterize the textures in medical images. Next, the approach introduces the latest meta-heuristic whale optimization algorithm (WOA), the global optimizer to enhance the performance of unsupervised clustering algorithm with optimized cluster centers to cluster the extracted wavelet texture features. Moreover, the study contributes to fill the gap in literature investigating for the first time different intelligence algorithms such as fuzzy, genetic algorithm (GA) and particle swarm optimization (PSO) for unsupervised medical image segmentation to demonstrate the efficacy of the proposed approach. Evaluation was performed on medical CT and MR images based on feature similarity (FSIM), dice (DC) and feature of merits (FOMs). Experimental results demonstrates the supremacy of the proposed approach over other intelligence algorithms. Finally, statistical study with ANOVA analysis was carried out to confirm the significance of the proposed approach in determining the optimal solution and displaying promising segmentation results toward diagnostic support for radiologist.

In today's interconnected era, intrusion detection system (IDS) has the potential to be the frontier of defense against cyberattacks and plays an essential role in achieving security of networking resources and infrastructures. The performance of IDS depends highly on data features. Selecting the most informative features eliminating the redundant and irrelevant features from network traffic data for IDS is still an open research issue. The key impetus of this paper is to identify and benchmark the potential set of features that can characterize network traffic for intrusion detection. In this correspondence, an ensemble approach is proposed. As a first step, the approach applies four different feature evaluation measures, such as correlation, consistency, information, and distance, to select the more crucial features for intrusion detection. Second, it applies the subset combination strategy to merge the output of the four measures and achieve the potential feature set. Along with this, a new framework that adopts the data analytic lifecycle practices is explored to employ the proposed ensemble for building an effective IDS. The effectiveness of the proposed approach is demonstrated by conducting several experiments on four intrusion detection evaluation datasets, namely KDDCup'99, NSL-KDD, UNSW-NB15, and CICIDS2017. The obtained results prove that the proposed approach contributes more potential features compared to the state-of-the-art approaches, leading to achieve a promising performance gain in the detection rate of 3.2%, the false alarm rate of 38%, and the detection time of 12%. Furthermore, ROC and statistical significance are analyzed for the identified feature subset to strongly conform its acceptability as a future benchmark for building an effective IDS.

Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data.