Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.

Huntington's Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington's disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis.

Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems.

The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad's Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities.

Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features.

Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.

Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.

The ongoing fast-paced technology trend has brought forth ceaseless transformation. In this regard, cloud computing has long proven to be the paramount deliverer of services such as computing power, software, networking, storage, and databases on a pay-per-use basis. The cloud is a big proponent of the internet of things (IoT), furnishing the computation and storage requisite to address internet-of-things applications. With the proliferating IoT devices triggering a continual data upsurge, the cloud-IoT interaction encounters latency, bandwidth, and connectivity restraints. The inclusion of the decentralized and distributed fog computing layer amidst the cloud and IoT layer extends the cloud's processing, storage, and networking services close to end users. This hierarchical edge-fog-cloud model distributes computation and intelligence, yielding optimal solutions while tackling constraints like massive data volume, latency, delay, and security vulnerability. The healthcare domain, warranting time-critical functionalities, can reap benefits from the cloud-fog-IoT interplay. This research paper propounded a fog-assisted smart healthcare system to diagnose heart or cardiovascular disease. It combined a fuzzy inference system (FIS) with the recurrent neural network model's variant of the gated recurrent unit (GRU) for pre-processing and predictive analytics tasks. The proposed system showcases substantially improved performance results, with classification accuracy at 99.125%. With major processing of healthcare data analytics happening at the fog layer, it is observed that the proposed work reveals optimized results concerning delays in terms of latency, response time, and jitter, compared to the cloud. Deep learning models are adept at handling sophisticated tasks, particularly predictive analytics. Time-critical healthcare applications reap benefits from deep learning's exclusive potential to furnish near-perfect results, coupled with the merits of the decentralized fog model, as revealed by the experimental results.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.

Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values.

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people's lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI's drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.

Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.

A framework to distinguish healthy/cancer renal CT images using the fused deep features.

Introduction: Cancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management. Methods: The proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation. Results and discussion: This experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.

MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net.

Brain tumor diagnosis has been a lengthy process, and automation of a process such as brain tumor segmentation speeds up the timeline. U-Nets have been a commonly used solution for semantic segmentation, and it uses a downsampling-upsampling approach to segment tumors. U-Nets rely on residual connections to pass information during upsampling; however, an upsampling block only receives information from one downsampling block. This restricts the context and scope of an upsampling block. In this paper, we propose SPP-U-Net where the residual connections are replaced with a combination of Spatial Pyramid Pooling (SPP) and Attention blocks. Here, SPP provides information from various downsampling blocks, which will increase the scope of reconstruction while attention provides the necessary context by incorporating local characteristics with their corresponding global dependencies. Existing literature uses heavy approaches such as the usage of nested and dense skip connections and transformers. These approaches increase the training parameters within the model which therefore increase the training time and complexity of the model. The proposed approach on the other hand attains comparable results to existing literature without changing the number of trainable parameters over larger dimensions such as 160 × 192 × 192. All in all, the proposed model scores an average dice score of 0.883 and a Hausdorff distance of 7.84 on Brats 2021 cross validation.

Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.

Degenerative nerve diseases such as Alzheimer's and Parkinson's diseases have always been a global issue of concern. Approximately 1/6th of the world's population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient's medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer's disease and Parkinson's disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases.

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images.

Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs' CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust architecture that will classify the COVID positive patients from COVID negative patients with less training. We have developed a neural network based on the number of channels present in the images. The CNN architecture is developed in accordance with the number of the channels present in the dataset and are extracting the features separately from the channels present in the CT-Scan dataset. In the tower architecture, the first tower is dedicated for only the first channel present in the image; the second CNN tower is dedicated to the first and second channel feature maps, and finally the third channel takes account of all the feature maps from all three channels. We have used two datasets viz. one from Tongji Hospital, Wuhan, China and another SARS-CoV-2 dataset to train and evaluate our CNN architecture. The proposed model brought about an average accuracy of 99.4%, F1 score 0.988, and AUC 0.99.

Psychological Impact and Influence of Animation on Viewer's Visual Attention and Cognition: A Systematic Literature Review, Open Challenges, and Future Research Directions.

Animation is an excellent method to associate with the audience in a fun and innovative manner. In recent span, animation has been employed in various fields to enhance knowledge, marketing, advertisement, and age groups from infants to adults. The present communication expounds the systematic review on the impact created by animation on the viewer's visual attention. For this review, a database such as Google Scholar, ScienceDirect, Taylor & Francis, and IEEE Xplore were pursued for publications on the impact of animation on viewer's visual attention from January 2015 to December 2021. The search results showcased 175 titles with 114 full articles, out of which 35 were related to viewers' visual attention towards animation. These reviewed studies comprised of physical outcome (n = 9), psychological outcome (n = 15), and cognitive outcome (n = 11) from which the attention-related factors, physical effects, and cognitive effects of animation were assessed. The animation has influenced the viewer's visual attention through the integration of the different stimuli and the highly organized presentation. Furthermore, the animation has also aided the viewer in attaining greater conceptual understanding, thereby facilitating their cognitive response. As a result, the animation was found to be helpful in enhancing learning skills, food marketing, and teaching strategy. Furthermore, the drawbacks and future recommendations of the studies were elaborated. In addition, challenges and open issues faced during the studies were discussed. Finally, the priority areas in animation identified for promising future directions to visualize large pool data, provide smart communication, and design 3D modeling structures were highlighted.

Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence Models.

Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluated on imagenet dataset. However, these models do not always translate to the best result on other datasets. Devising a neural network manually from scratch based on heuristics may not lead to an optimal model as there are numerous hyperparameters in play. In this paper, we use two nature-inspired swarm algorithms: particle swarm optimization (PSO) and ant colony optimization (ACO) to obtain TDCN models to perform classification of fundus images into severity classes. The power of swarm algorithms is used to search for various combinations of convolutional, pooling, and normalization layers to provide the best model for the task. It is observed that TDCN-PSO outperforms imagenet models and existing literature, while TDCN-ACO achieves faster architecture search. The best TDCN model achieves an accuracy of 90.3%, AUC ROC of 0.956, and a Cohen's kappa score of 0.967. The results were compared with the previous studies to show that the proposed TDCN models exhibit superior performance.

Multimodal Medical Image Fusion of Positron Emission Tomography and Magnetic Resonance Imaging Using Generative Adversarial Networks.

Multimodal medical image fusion is a current technique applied in the applications related to medical field to combine images from the same modality or different modalities to improve the visual content of the image to perform further operations like image segmentation. Biomedical research and medical image analysis highly demand medical image fusion to perform higher level of medical analysis. Multimodal medical fusion assists medical practitioners to visualize the internal organs and tissues. Multimodal medical fusion of brain image helps to medical practitioners to simultaneously visualize hard portion like skull and soft portion like tissue. Brain tumor segmentation can be accurately performed by utilizing the image obtained after multimodal medical image fusion. The area of the tumor can be accurately located with the information obtained from both Positron Emission Tomography and Magnetic Resonance Image in a single fused image. This approach increases the accuracy in diagnosing the tumor and reduces the time consumed in diagnosing and locating the tumor. The functional information of the brain is available in the Positron Emission Tomography while the anatomy of the brain tissue is available in the Magnetic Resonance Image. Thus, the spatial characteristics and functional information can be obtained from a single image using a robust multimodal medical image fusion model. The proposed approach uses a generative adversarial network to fuse Positron Emission Tomography and Magnetic Resonance Image into a single image. The results obtained from the proposed approach can be used for further medical analysis to locate the tumor and plan for further surgical procedures. The performance of the GAN based model is evaluated using two metrics, namely, structural similarity index and mutual information. The proposed approach achieved a structural similarity index of 0.8551 and a mutual information of 2.8059.

Biometric Authentication for Intelligent and Privacy-Preserving Healthcare Systems.

Secure identification is a critical system requirement for patients seeking health-related services. In the event of critical, aged, or disabled patients who require frequent health treatments, quick and easy identification is vital. Researchers describe the notion of the unprotected environment in this study, in which patients can receive health services from the hospital's smart and intelligent surroundings without the use of explicit equipment. Patients would interact directly with the environment and be identified through it. We suggest a biometric-based authentication technique for the unprotected hospital environment that also safeguards the patient's identity privacy. Furthermore, we demonstrate that this authentication technique is resistant to many well-known assaults, including insider attacks, replay attacks, and identity privacy. Doctors and other staff members showed enthusiastic responses after installing 2-factor authentications, as it makes their workflow efficient and makes things easier for patients. It also lets them focus on other factors rather than worrying about data security; hence, we need biometric authentication in intelligent and privacy-preserving healthcare systems. The paper deals with two-factor biometric authentication, and despite the added security, two-factor authentication adoption is said to be poor. It is due to a lack of awareness and difficulty to use and configure two-factor authentication (2FA) into a particular application by some individuals who struggle with the concept of authentication and its technology. Also, many 2FA methods in widespread use today have not been subjected to adequate usability testing. Research focuses on the point that there is still a large section of people unaware of the use of biometric systems to protect their online data. Researchers collected quantitative and qualitative data from 96 individuals during a two-week between-subjects usability survey of some common and rarely used 2FA approaches. The survey allowed the researcher to investigate which authentication methods are given higher priority and why, along with the relationship between different usage patterns and perceived usability, and identify user misconceptions and insecure habits to determine ease of use. It was observed that the biometric-based method was given the utmost preferability.

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification.

Understanding the reason for an infant's cry is the most difficult thing for parents. There might be various reasons behind the baby's cry. It may be due to hunger, pain, sleep, or diaper-related problems. The key concept behind identifying the reason behind the infant's cry is mainly based on the varying patterns of the crying audio. The audio file comprises many features, which are highly important in classifying the results. It is important to convert the audio signals into the required spectrograms. In this article, we are trying to find efficient solutions to the problem of predicting the reason behind an infant's cry. In this article, we have used the Mel-frequency cepstral coefficients algorithm to generate the spectrograms and analyzed the varying feature vectors. We then came up with two approaches to obtain the experimental results. In the first approach, we used the Convolution Neural network (CNN) variants like VGG16 and YOLOv4 to classify the infant cry signals. In the second approach, a multistage heterogeneous stacking ensemble model was used for infant cry classification. Its major advantage was the inclusion of various advanced boosting algorithms at various levels. The proposed multistage heterogeneous stacking ensemble model had the edge over the other neural network models, especially in terms of overall performance and computing power. Finally, after many comparisons, the proposed model revealed the virtuoso performance and a mean classification accuracy of up to 93.7%.

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Today's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions.

With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.