The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term ’security’ encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

Load forecasting in Smart Grids (SG) is a major module of current energy management systems, that play a vital role in optimizing resource allocation, improving grid stability, and assisting the combination of renewable energy sources (RES). It contains the predictive of electricity consumption forms over certain time intervals. Load Forecasting remains a stimulating task as load data has exhibited changing patterns because of factors such as weather change and shifts in energy usage behaviour. The beginning of advanced data analytics and machine learning (ML) approaches; particularly deep learning (DL) has mostly enhanced load forecasting accuracy. Deep neural networks (DNNs) namely Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) have achieved popularity for their capability to capture difficult temporal dependencies in load data. This study designs a Short-Load Forecasting scheme using a Hybrid Deep Learning and Beluga Whale Optimization (LFS-HDLBWO) approach. The major intention of the LFS-HDLBWO technique is to predict the load in the SG environment. To accomplish this, the LFS-HDLBWO technique initially uses a Z-score normalization approach for scaling the input dataset. Besides, the LFS-HDLBWO technique makes use of convolutional bidirectional long short-term memory with an autoencoder (CBLSTM-AE) model for load prediction purposes. Finally, the BWO algorithm could be used for optimal hyperparameter selection of the CBLSTM-AE algorithm, which helps to enhance the overall prediction results. A wide-ranging experimental analysis was made to illustrate the better predictive results of the LFS-HDLBWO method. The obtained value demonstrates the outstanding performance of the LFS-HDLBWO system over other existing DL algorithms with a minimum average error rate of 3.43 and 2.26 under FE and Dayton grid datasets, respectively.

Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the Gazelle Optimizer Algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods.

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

Audits are an essential component of every organization, particularly those involving software development. In addition to several testing cycles, software auditing has become an essential software development milestone. Software auditing is a continual activity that enables a business to remain ahead of the curve and predict potential software problems. Audits, whether undertaken in-house or by external auditors, entail a significant amount of time and work. Consistent audits provide financial and economic benefits, as well as legal benefits. The most essential advantage of audits is safeguarding your system from internal and external assaults. Audit logs serve a crucial role in the auditing process; they typically capture all system operations and occurrences. They are used as evidence providers during an inquiry and by auditors to monitor the privacy and security of information and systems. Auditors confirm the accuracy of data pertaining to businesses and their activities. To determine if these acts exceed the limitations established by organizations, governments, and other parties, dependable information is essential. Infractions of such rules or corporate standards may be indicative of fraud, malpractice, risk, or inefficiency. Despite the existence of automated audit tools, audit policy, and audit logs, many audit frauds are reported on a daily basis. To make the audit process transparent and secure, this research proposes a blockchain-enabled framework SSFTA to aid software auditors in conducting a transparent and effective audit process. The proposed framework is evaluated using a case study. The findings demonstrated that the suggested framework makes the auditing process simple and transparent.