The advent of artificial intelligence (AI) techniques has revolutionized network security by enabling predictive modelling for threat detection. This abstract proposes a novel approach to enhancing network security through predictive modelling, leveraging advanced AI techniques. By analyzing vast amounts of network traffic data, AI algorithms can identify patterns indicative of potential threats, including malware, intrusions, and anomalous activities. The predictive models developed through this approach can forecast potential network vulnerabilities and pre-emptively detect emerging threats before they manifest into security breaches. This proactive stance empowers organizations to fortify their network defenses, minimize the risk of cyberattacks, and safeguard sensitive information. Through the fusion of AI and predictive modelling, this research endeavors to pave the way for more robust and resilient network security frameworks in an increasingly interconnected digital landscape.

WSNs are still highly susceptible to routing failures and energy depletion, as well as malicious intrusions, due to their multi-node configuration and stringent resource limitations. This manuscript considers the hypothesis that performing joint cluster-formation and routing optimization together with deep learning-based intrusion classification can elevate the network's dependability and cybersecurity framework. A novel optimized path selection and deep learning-based attack detection framework is presented, therefore, to validate this hypothesis. The methodology integrates three major components: Network nodes are first deployed randomly and then clustered by Ip-GJOA, which performs an optimal selection of cluster heads based on their spatial correlation, link quality, and residual energy. Trust management was incorporated by assessing each node's trust coverage so that only reliable nodes can be assigned as cluster heads. Next, SOA was employed to isolate the best power-efficient option and trustworthy routing paths between clusters. After these steps, intrusion detection and classification are carried out by employing a dual-stream recurrent-convolutional network design that combines the spatial learning ability of convolutional neural networks with the temporal modeling capability of recurrent layers. The overall framework is implemented in Python and tested against state-of-the-art security and clustering techniques for WSNs. Experimental results demonstrate an accuracy of 0.811, an average delay of 3s, a throughput of 99%, and a detection rate of 0.88 are obtained by the developed framework. With these results, confirmation is made that the proposed approach achieves secure routing and trust-aware cluster formation and improves the intrusion classification performance in WSNs.

Lung illness is any condition that impairs the lungs' capacity to function normally; diabetes, in particular, can have a variety of effects on the lungs and increases the risk of respiratory infections such as pneumonia and TB in those who have it. According to recent studies, 384 million individuals have chronic obstructive pulmonary disease (COPD), and 3 million of them pass away each year as a result of delayed diagnosis. Early detection of any disease can lessen its severity, which will lower the death rate and number of lung disease sufferers. Used deep learning methods for lung detection diagnostics, such as improved fish bee (IFB) and chaotic bird swam optimization (CBSO), despite the prediction rate's high false rate and lack of accuracy. Introduced a technique using machine learning methods, such as the Principal Component Analysis (PCA) algorithm for extraction of features, the Decision Tree approach for sample categorization, and the Support Vector Machine (SVM) for very accurate early illness diagnosis. The results of the suggested approach for early lung tumor identification with a low incorrect rate showed an accuracy of 98.07.

Diabetic kidney disease (DKD) is a diabetic condition in which elevated blood sugar levels harm the kidney's filtering units, leading to kidney damage and potentially, kidney failure. About 700 million people effected by Diabetic Kidney Disease of whom approximately four million patients require kidney replacement therapy (KRT). The existing methods for predicting Diabetic Kidney Disease (DKD) have several drawbacks that limit their effectiveness and accuracy. Current models often rely on traditional biomarkers, such as blood glucose and urine albumin levels, which can be insufficient for early detection as they typically indicate kidney damage only after it has progressed. Proposed a novel technique by utilizing machine learning algorithm such as navie Bayes to predict the DKD in early stage leads to decrease the mortality rate. Proposed invention enhances the accuracy in disease diagnosis, which result in the score of 96.03, 94.03, 95.08 and 95.09 with precision, accuracy recall and F1-Score respectively.

This manuscript proposes an attention segmental recurrent neural network (ASRNN) optimised with sheep flock optimisation based intrusion detection scheme for securing internet of things (IoT) environment. Initially, the data is fed to pre-processing, wherein, the redundancy eradication and missing value replacements are performed by random forest and local least squares (LLS). Afterward, pre-processing data is supplied to the feature selection to select optimal features. The correlation feature selection-based processing of feature selection is done. The selected features are fed to attention segmental recurrent neural network, which categorises the data as normal or anomalies. Finally, sheep flock optimisation (SFO) is considered to optimise the ASRNN. The simulation performance of the proposed technique attains better accuracy 20.56%, 18.67%, 23.77%, 38.45%, 22.75%, 36.45%, higher precision 42.36%, 22.15%, 56.45%, 22.03%, 28.63%, and 21.36% compared with the existing methods.

As the competition for higher education and professional employment intensifies globally, the proper evaluation and translation of academic credits and qualification systems has taken on a key significance. Most classic techniques for evaluating the contents of equivalence course certificates, such as Jaccard Index or Cosine, are too simplistic and do not reveal differences in educational credentials or course materials that are found at a semantically deeper structure. These techniques are primarily based on the surface-level characteristics of the texts, ignoring their inherent pictures. To overcome these challenges, this paper considers the possibility of using methods of Natural Language Processing (NLP), especially Word Mover's Distance, in the context of higher education and professional qualification, academic credit and course equivalency evaluation. WMD, or Word Mover's Distance, is a more sophisticated method of NLP, which describes the food transport distance problem and searches for the optimal distance between two different documents. The advantages of this methodology lie in comparing the course description and qualifications from a different perspective without losing the true meaning of these words in relation to course equivalence certificate comparison. On the basis of our study, we can state that WMD implements better than other conventional approaches and is an appealing solution for credit system transfer, the degree equivalence process evaluation in higher education, and employment clearance services. In this way, WMD applications for NLP purposes will allow for the sustainable recognition of the international qualifications of professionals and students, thus enhancing their movement across borders.

Cardiomyopathy is the type of heart disease that results from diabetes that influences the heart muscles, leading to structural and functional abnormalities and these situations can weaken the heart muscle, loss its ability to supply blood effectively. Cardiomyopathy affects millions worldwide every year, with an estimated 1 in 500 people impacted. In the United States, it leads to approximately 50,000 deaths are reported annually. The existing method with Logistic Regression for the detection of Cardiomyopathy obtained inaccurate results with a high false alarm rate. Introduced a deep leaning convolution neural networks algorithm for the preprocessing, extraction of data selection of features and diagnosis of disease with accurate results in the early stage. As a result, got the accurate performance for accuracy, precision, sensitivity, and recall with 97%, 96%, 95.6%, and 96% respectively.

As digitalization permeates all aspects of life, the Internet has become a critical platform for communication across various domains. Workstations within organizations often handle sensitive and private data, underscoring the need for encryption to safeguard information and prevent unauthorized access. Despite advances in system security, challenges remain in the form of system vulnerabilities and evolving cyber threats. Intrusion detection using deep learning (DL), which serves as the second line of defense after firewalls, has progressed rapidly, yet still faces issues such as misclassification, false positives, and delayed or inadequate responses to attacks. These ongoing problems necessitate continuous improvement in system security screening and intrusion detection to protect networks effectively. Therefore, in this research, a novel DL framework called capsule convolutional polymorphic graph attention neural network with tyrannosaurus optimization algorithm (CCPGANN-TOA) is utilized for attack detection due to its advanced feature representation, graph attention for focusing on key data points, polymorphic graphs for adaptability, and TOA for performance optimization. Normal data are then encrypted using the digital signature algorithm based on elliptic curve cryptography (DSA-ECC) because it provides strong security with smaller key sizes, resulting in faster computations and efficient resource utilization. The proposed method outperforms traditional approaches in terms of 99.98% accuracy of data set I, 99.9% accuracy of data set II, and 900 Kbps higher throughput with low delay.

This research provides through method for building a recommendation system for health concerns for women the significant number of individuals’ day-to-day activities are affected by menstruation symptoms including pains, mood swings and fatigue. Be it making an individual food prescription or eradicating these symptoms, nutrition is an absolute factor. Thus, according to this work, there is a concept of a method that based on machine learning will help predict menstrual symptoms and provide tailored nutrition recommendations. The analysis of the respondents scores for six menstrual cycles revealed that magnesium, omega-3 fatty acids, vitamin D, and calcium are significant nutrients affecting the symptom severity level. Various Machine Learning models to compared include the Neural Networks, Support Vector Machines and Random Forests. Out of all the models the highest was achieved by the Random Forest model for the targeted recommendations of the proper nutrition adjusted to the person, where the prediction accuracy was 98%. The findings of the provided results support the view that is hope and the intensity of menstruation symptoms reduced significantly.

This study proposes a unique sensor plus machine learning system designed to enhance patient care and targeted medicine administration, with a specific focus on controlling Cryptococcosis, in the search for more precise and proactive healthcare solutions. This research had contributed to the conceptualization, design and implementation of system which integrates constant sensor data monitoring and machine learning algorithms to anticipate and prepare for any health problems. Over the course of a day, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF) were used to analyse sensor data from 10 patients with Cryptococcosis. The sensor data gathered from these people included information on their body temperature, blood pressure, oxygen saturation, heart rate, blood sugar levels, and ECG measurements. The models executed flawlessly, achieving F1 scores for accuracy, recall, and precision. SVM won with a precision score of 97.6%, followed by ANN with a score of 95.3% and RF with a score of 92.1%. The models' capacity to get rid of misclassifications and provide precise predictions needed for right away action was demonstrated by the confusion matrices that went along with them. This discovery, which might revolutionise how patients are treated for difficult-to-treat illnesses like cryptococcosis, is an encouraging first step in the direction of proactive healthcare management. The sensor and machine learning system offers a patient-centered, data-driven approach that keeps up with the changing landscape of healthcare.

Breast cancer (BC) is one of the most widespread kinds of cancer that affects women. Mammography is the most employed imaging modality for detecting BC. Earliest BC detection is a vital phase for effective treatment of disease. The mortality from BC can be lessened by detecting and recognizing it at an earlier stage. Here, Quantum SpinalNet (Q-SpinalNet) is introduced for detecting BC utilizing mammogram (MG) images. An input MG image is taken from a definite database initially and it is then pre-processed. Non-Local Means Filter (NLM) filter is employed for pre-processing an image. Thereafter, the segmentation of the images is carried out utilizing ET-SegNet which is an integration of Edge-aTtention guidance Network (ET-Net) with Segmentation Network (SegNet). These two networks are fused based on the Random Variable (RV) coefficient. After that, features namely Local Ternary Pattern (LTP), Fuzzy Local Binary Patterns (FLBP), statistical features, Pyramid Histogram of Orientation Gradients (PHoG) and Median Binary Patterns are extracted. At last, BC detection is accomplished by Q-SpinalNet, which is designed by amalgamating Deep Quantum Neural Network (DQNN) with SpinalNet. Furthermore, Q-SpinalNet obtained 90.3% of accuracy, 90.9% of True Negative Rate (TNR) and 90% of True Positive Rate (TPR).

To interpret the MRI data, the study uses Convolutional Neural Networks (CNN), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), and Naive Bayes (NB). In this article, the use of machine learning models for breast cancer diagnosis is carefully examined. The experiment's findings revealed numerous interesting findings and conclusions. The CNN model fared the best and had the most promise with an accuracy score of $\mathbf{9 6. 4 \%}$. This finding reveals the accuracy with which deep learning systems can identify breast cancer in imaging data. This astonishing level of accuracy was made possible by the CNN's expertise in feature extraction and image identification. KNN also performed well, demonstrating its applicability for context-aware breast cancer detection with an accuracy of 93.7 %. It has been demonstrated that the KNN's proximity-based classification algorithm can accurately classify related events with high recall and precision rates. Despite being slightly less accurate than CNN and KNN, SVM and NB fared brilliantly, with accuracy rates of 92.2 % and 90.33 percent, respectively. These models demonstrated how machine learning may provide flexible and effective diagnostic skills for the diagnosis of breast cancer.

The advent of artificial intelligence with various optimization techniques has revolutionized the industry of drug development by providing an accurate prediction of protein targets for the development of therapeutic compounds. The proposed system provides an elaborative concept combining deep learning with optimization techniques for the recognition of protein targets for drug development. The first stage involves the collection of datasets that include various chemical compounds that are pre-processed to maintain consistency and stability in the system. The chemical structures are represented using molecular fingerprints. The interrelationships and the complex patterns between the chemical structures and the protein targets are observed using deep learning models such as Convolutional Neural Network (CNN) and Graph Neural Network (GNN). The optimization techniques are involved to maintain the performance of the proposed model. The optimal configuration of the system model and its parameters is identified using the Bayesian optimization technique. The model is trained on a pre-trained compound protein dataset which helps to extract the intricate mapping between chemical structures with associated target proteins. The minimization of the loss function by monitoring the convergence using a validation dataset is achieved in the training process. The predictive accuracy of the model is obtained through rigorous evaluation using separate test datasets. The performance of the model is observed through certain metrics such as accuracy, precision, recall, F1 score and ROC curves. Thus the proposed system provides optimal results in accurate recognition of human protein targets for the development of drugs. This leads to a time-consuming process compared with traditional methods in drug development. This helps to enhance the development of novel drugs for various diseases.

The process and implementation of drug development is a complex structure which is a time-consuming process with higher accuracy. A transformative approach to optimize drug discovery is achieved through the integration of drug discovery with a virtual screening process. This is implemented through the in silico machine learning process. The traditional methods involve extracting and testing numerous chemical compounds in vitro and in vivo in the identification of potential drug contenders. These methods lead to various drawbacks that include higher costs with timelines. To overcome the drawbacks of the existing system, the integration of paradigm modeling is initiated. The behavior of molecules with biological targets is determined through In-silico machine learning algorithms. The effective analysis of millions of chemical compounds is done using a virtual screening process through computational simulations and desired pharmacological effects. The machine learning algorithms help in the extraction of intricate patterns from the molecular dataset. Certain properties such as molecule binding affinity, bioavailability, and toxicity are predicted using these algorithms. They help to optimize the molecular structure in silico to improve the interaction with the target proteins. The reliability is achieved through the quality of the training data with the robustness of the algorithm. They help to generalize newer chemical combinations that provide the solution for various diseases with personalized recommendations through the aid of artificial intelligence.

MicroRNAs (miRNAs) are integral components of genomic data, offering valuable insights into genome sequences. Existing studies have faced challenges in achieving desired classification accuracy. To address this issue, Syntax -Guided Hierarchical Attention Network optimized with Golden Jackal Optimization (SGHAN-GJOAMiRNA-BC) is proposed to classify cancer miRNA biomarkers. Input data is amassed from Cancer Genome dataset. Data pre-processing uses the Z -score normalization method for standardizing the data. The stochastic gradient enhancement -based recursive feature elimination (SGB-RFE) method selects relevant features. Then the Syntax -Guided -Hierarchical Attention Network (SGHAN) classifies cancer miRNA biomarkers for diagnosis, therapy, and prognosis. The Golden Jackal Optimization (GJO) algorithm optimizes SGHAN ensuring precise classification. The efficiency of proposed SGHAN-GJOA-MiRNA-BC technique is evaluated. For cancer genome atlas dataset, it achieves increase in accuracy of 10.95 %, 24.45 %, 11.85 %, 12.87 %, 16.58 %, 10.99 % and the existing techniques Cancer MiRNA biomarker categorization using generative adversarial network (GANMFOA-MiRNA-BC), lung cancer detection utilizing hybrid deep neural network (ML-ABCOA-MiRNA-BC), convolution neural network for breast cancer categorization under RNA-Seq-data (CNN-EOSA-MiRNA-BC), Parallel Bayesian model for breast cancer prediction(DNN-ASCCS-MiRNA-BC), Biomarker identification and cancer survival prediction using Bayesian optimized-DNN (BODNN-CSOA-MiRNA-BC), identifying ovarian cancer 's potential biomarkers (SVM-ICA-MiRNA-BC), a machine learning method for pancreatic cancer diagnosis (ANN-PSO-MiRNA-BC) respectively.

The disease that causes a large number of deaths annually across the world is brain cancer and it has become an important research topic in the field of medical image processing in recent times. There are various techniques for the detection of brain tumors (BT) but magnetic resonance imaging (MRI) diagnosing techniques show superior performance in the prognosis and examination of brain tumors in the early stages. The manual detection of brain tumors by radiologists leads to many limitations like errors and lack of detection accuracy. Hence, there is a need for computer-aided diagnostic techniques to help radiologists in detecting brain tumors accurately from the MRI images. To make this process more effective, the implementation of an automated technique is a preferred choice. In this paper, an effective detection and classification technique Adaptive convolutional Autoencoder-based Snow Avalanches (ACAE-SA) Algorithm is proposed. This algorithm comprises an Adaptive CNN component and an Autoencoder to detect and categorize BT from the MRI images. To mitigate the computational complexities in these components a Snow Avalanches algorithm is integrated into this work as an optimization technique. For the validation of the proposed architecture two MRI image datasets namely figshare and BraTS 2018 are used. The proposed technique proved its effectiveness in the detection and classification of brain tumors from the MRI images and outperformed the state-of-the-art techniques.

Mycosphaerella Coffeeicola (Black Spot Disease) is the most destructive disease in coffee leaves and affects global production. Diseased coffee leaves can adversely affect coffee quality, discolor, distort, or rot the coffee leaves, and impair the coffee beans’ cherry, taste, and aroma. The disease can also affect the quality of the coffee beans, leading to lower market value. The management of the Coffee Production Department of India controls the disease through a combination of cultural practices, chemical control, and the use of resistant coffee varieties. In the existing work, using single-layer perceptron was trained and validated with 100 epochs to obtain an accuracy of $\mathbf{9 5. 7 1 \%}$. In the piece above of work, an infected leaf is accurately diagnosed using deep learning-based models by employing the Deep-Siamese-based CNN Approach, recognized by a deep Siamese Neural network architecture which is also called a Twin Neural network with high illness recognition precision and simplified hyper-parameters to maximize description capacity while avoiding overfitting concerns, using a network with high sickness detection precision and minimal hyper-parameters. The prototype was tested in the lab on natural plants for known sickness instances, and the results matched the model validation datasets. When compared to several deep-learning models, the suggested structure attained an accuracy of $\mathbf{9 6. 8 9 \%}$.

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets.

The dark web is an overlay network comprised of the darknet, which can only be accessed via specialised software and a predetermined permission scheme. This article investigates the development of dark web intelligence as a means of enhancing cybercrime prevention tactics in several countries. On the basis of machine learning, we develop, analyse, and assess the effectiveness of darknet traffic detection systems (DTDS) in IoT networks. We focused at the safety features that are available to users, as well as their motivations and the ability to revoke their anonymity. In addition, we perform a depth analysis by automating the process of detecting hostile intent from the darknet. Finally, we compared our proposed system to various already existing DTDS models and showed that our best results are an improvement of between 1.9% and 27% over the models that were previously considered to be state-of-the-art.

This research provides a comprehensive approach in designing a recommendation system for women’s health issues, with emphasis on menopause and optimal eating plan with Shatavari nutrition plan incorporated. The data includes symptom severity; heart rate; temperature; sleep; and food and water intake, which has been obtained from patients’ medical records, structured surveys, the users themselves, and through healthcare apps. Handling of missing values involves the completion of the missing records by forward method and imputation. Kindly find below the steps taken to pre-process the given data set for its numerical and categorical features respectively; Standard Scaler for numerical data set and OneHotEncoder for the binary data set. Variable creation is used to improve the performances of the models by creating new variables from other variables. Raw, selected and vectorized features are used to perform and compare multiple machine learning algorithms such as linear regression, logistic regression, support vector machines, decision random forest and gradient boosting, XGBoost, LightGBM, and CatBoost. The present study reveals that LightGBM has the maximum accuracy of $96 \%$. The proposed system combines collaborative and content-based filtering, which will provide accurate and individual health and lifestyle recommendations for enhancing the quality of women’s life during menopausal transition.