This paper proposes a multiscale residual framework for gender classification on fingerprint images. It addresses the tough problem where most gender-related features in fingerprint ridges are subtle, and previous methods relied mainly on spatial features and failed to utilize the multi-scale features, attention, and dimension reduction. Therefore, morphological preprocessing, discrete wavelet-based multiscale feature extraction, Siamese network-based dimensionality reduction, and residual neural network classification, involving attention and non-local block, is proposed to effectively solve this problem. This framework performs morphological preprocessing on fingerprint images and decomposes the image into low-, mid-, and high-frequency channels by discrete wavelet transform; then, these channels are embedded into feature vectors by a Siamese ResNeXt-SE structure, and their cross-attention is fused to retain inter-/intra-class discriminability. Then, it applies a residual neural network with attention and a non-local block on the features for classification. On the SOCOFing dataset, which has 6,000 fingerprint images, the accuracy, precision, recall, and F1-score are 99.50%, 99.68%, 98.82%, and 0.99, respectively, and AUC is 0.99 for both genders, which achieves excellent results. Ablation studies have proven that morphological operators, wavelet decomposition, and Siamese compression do contribute to the framework individually. Experiments against two common types of noises, Gaussian and salt-and-pepper, also demonstrate the robustness against noise with trivial loss of accuracy, and comparing with prior CNN and CNN-SVM models, the method shows superiority by utilizing multiscale and frequency domain information comprehensively. However, it is constrained to use high-quality grayscale images and is not tested on adversarial robustness. The framework shows high potential for applications such as automatic fingerprint identification systems, forensics, and large biometric databases since the gender classification is effective, scalable, and discriminative.
The widespread adoption of digital learning platforms has led to increased deployment of user behavior analytics technologies to enhance learner engagement, predict their academic performance, and provide personalized learning services. Nevertheless, traditional approaches to learning raise some security issues related to the privacy violation of educational data and its possible misuse. In order to address the aforementioned challenges, this paper introduces a Federated Learning-Based Privacy-Preserving User Behavior Analytics approach (FL-PPUBA). The suggested technology allows a number of educational organizations and clients' devices to train behavioral prediction models based on data without disclosing any learner information. The algorithm uses adaptive federated averaging, encrypted parameter communication, secure aggregation, and decentralized neural network training. To conduct a computational experiment, an e-learning behavior analytics dataset including 120,000 instances was used. The framework was developed through the use of TensorFlow Federated, TensorFlow, and encrypted techniques in heterogeneous non-IID data. Evaluation of the performance was done by measuring various metrics such as accuracy, precision, recall, F1-Score, privacy preservation, and communication efficiency. The proposed FL-PPUBA framework performed better than current centralized and federated learning frameworks. Experimental findings show that the framework scored 96.4% accuracy, 95.8% precision, 95.1% recall, and 95.4% F1-Score. In addition, it obtained a privacy preservation rate of 98.1% and 91.6% communication efficiency. This indicated the performance was efficient and privacy-preserved in federated learning. Ablation studies show the contribution of both the adaptive aggregation technique and the encrypted technique in enhancing convergence stability and privacy. Based on the above analysis, it can be concluded that federated learning can provide an appropriate solution in developing privacy-preserving user behavior analytics frameworks in future e-learning applications. The next phase could consider other aspects such as blockchain technology, explainable AI, and federated intelligence at the edge level.
The problem of correctly predicting the intensity of online purchasing proves still to be very challenging due to scattered behavioural cues, extreme class imbalance and the increasing importance of trust and security in the decision-making process of consumers. Purchase prediction models currently in use have mostly been based on single behavioural measures, i.e. click rate, purchase history, etc. Single behavioural measures are not sufficient to capture the multidimensionality of purchase intention in actual e-commerce settings. To overcome these drawbacks in current paper proposes the development of Security-Conscious Composite Behavioural Intelligence (SC-CBI) model for the forecasting of online purchasing intensity at multiple levels. The proposed model introduces Composite Behavioural Indices (CBI) that introduce behavioural engagement, temporal consistency, monetary commitment and security-induced trust interactions in a low dimensional representation of user intent. In addition, a Security-Aware Balanced Classification Strategy (SABCS) will be developed in order to overcome the severe class imbalance and to preserve trust-related behavioural distributions by adaptive resampling and risk-sensitive cost optimisation. Tests on large-scale, real-world e-commerce data show that the given framework is better than traditional feature-based and imbalance-blind models. The SC-CBI model has an average accuracy of 92.7, a macro F1-score of 90.8 and a balanced recall of 90.2 which can improve up to 8-12 points compared to baselines from strong ensembles. The given framework is most appropriate for implementation in the contemporary e-commerce systems that require precision decision support in the security impaired and data imbalanced environment.
Smart agriculture uses technology to enhance farming processes, with Unmanned Aerial Vehicles (UAVs) as a crucial tool for aerial imaging, crop monitoring and precision spraying. The inability of existing research to precisely identify spray regions impedes the effective control of pests and diseases in agricultural settings. In this study, we present the development of the Clonal Flower Pollination integrated Adaptive radial deep neural network (CFPO-ARDNN), which uses data from UAV s to identify spray zones. The classifier was evaluated in agricultural settings using an RGB camera, considering five distinct croplands. To assess and test the created system, high-resolution UAV photos were employed in five distinct target fields: pea, apricot, strawberry, peach and coriander. The Joint Photographic Experts Group (JPEG) converter, RGB to hue saturation intensity (HSI), Wavelet Transform and Local Binary Pattern (LBP) for feature extraction were all used in the preparation of the video. In comparison, the CFPO-ARDNN model outperformed the others in predicting the following: strawberry (91.85%), Apricot (96.56%), Peach (95.67%), Coriander (93.47%) and pea (94.45%). The experiment’s findings demonstrate how well the suggested CFPOARDNN model works in comparison to the current approaches.
The ubiquitous development of mobile devices, especially Android-based operating system, has provided a good environment of malicious software (malware) to steal user data and system integrity. Android is open-source in nature and has a large size of applications, which makes good, timely detection of malware a significant security problem. To overcome this, the paper offers a new and evidence-based model of efficient malware detection based on various machine learning (ML) models. It uses the methodology that targets a static analysis, which employs a set of discriminatory features, including sought dangerous permissions and suspicious API calls as major elements of a comprehensive set of discriminatory features, extracted out of Android Application Packages (APKs). The output of four popular ML classifiers, namely, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB) was tested on a big, real-life dataset. The outcome of the experiment proves that the Random Forest classifier performed greatly better than the other classifiers, giving an accuracy of 98.5 and an F1-score of 98.3. This high performance justifies the performance of the chosen static features to draw a line between the benign and malicious applications. The model obtained provides a strong, highly precise, and computationally efficient solution that can be incorporated into real-time security systems to enhance the defensive capability of the Android platform against ever-changing threats.
Secure and reliable operations with information are needed for the sustainability of a context-aware mobile open learning environment. Students' attendance prediction can be maximized to optimize resource usage, enhance operational effectiveness, and increase confidence in the institution. The challenge is that conventional statistical techniques don't take into account temporal, contextual, and resource security issues in a decentralized mobile learning setting. This paper presents a model that provides the background for LSTM networks (Long Short-Term Memory) in the context of realtime student attendance prediction. The model can capture an intricate time series while preserving confidential student data, and possessing a touch of privacy-protected lightweight mobile streams keeps the model's ability to interface intact. In the evaluation of the model within the framework, it is evident that the model exhibits satisfactory performance (training: accuracy = 99%, precision = 98%, recall = 99%, F1 = 98%). Furthermore, the model's performance has been sustained even in the presence of adversarial attacks, data silos, and data exfiltration. The MAX LOAD behavior is considered good support for the viability of the model and as supportive evidence that AI-based prediction in the mobile environment can be undertaken with reasonable confidence. This effort addresses the task of reasonably integrating predictive modeling into the problem of obtaining appropriate measures of security for use in modeling, towards intelligent mobile learning systems that are adaptive, sustainable, private yet trusted, and hence maximize institutional trust in digital learning spaces.
The propose Fed-DRL++, a new framework that combines Personalized Federated Learning (PFL) with Hierarchical Deep Reinforcement Learning (HDRL) and Edge Aggregation. This setup enables real-time, decentralized, and privacy-friendly traffic management in smart cities. Unlike traditional centralized methods, Fed-DRL++ uses a layered decision-making structure. It applies Dueling DQN at the route level and a mix of Dueling DQN and PPO at zone and junction levels. This approach adjusts to local traffic conditions by using both historical and real-time mobility data. A key innovation is the use of Prioritized Experience Replay with Hindsight (HERPP). This method focuses learning on significant events like congestion and collisions. Edge aggregation lowers communication overhead by performing local updates before syncing with the cloud. Meanwhile, PFL maintains model performance even with non-IID data across users. In tests with 1,000 mobile agents across five congestion scenarios, Fed-DRL++ outperformed benchmarks. It reduced travel time by 23.4%, improved route convergence by 31%, and cut congestion by 18%. It also decreased communication costs by 41.6% and boosted prediction accuracy (RMSE: 2.91 km/h, MAE: 2.06 km/h), with results being statistically significant (p < 0.01). This shows its effectiveness for scalable and smart traffic optimization.
Product Lifecycle Management (PLM) emerged from the growing need for companies to oversee their products throughout their entire lifecycle—from design and production to usage, and even disposal. This responsibility includes managing product data, regardless of whether the product remains within the company, is in use by customers, or has reached the end of its life in a landfill. PLM systems aim to centralize and allow editing of all product-related data across departments and stages. However, integrating such comprehensive tools into industrial environments is often challenging. The diversity of professional roles each with distinct cultures, expertise, and technical languages complicates seamless adoption and efficient use of PLM systems. To address this challenge, Digital Adoption Platforms (DAPs) have emerged as complementary tools. DAPs are software layers that integrate with existing applications to guide users through processes via step-by-step tutorials, contextual tooltips, and input suggestions. While DAPs have proven effective in many enterprise software domains by easing onboarding and increasing productivity, their use within PLM systems remains limited and under-researched. Particularly, there is a lack of data on how DAPs affect the usability of complex PLM systems and whether they offer tangible improvements for novice users. This study aims to evaluate the usability of PLM systems enhanced by DAPs. By analyzing the user experience and measuring performance, the research seeks to determine whether DAP assistance leads to better adoption, reduced training time, and improved user satisfaction. The findings will also help identify the underlying reasons behind any observed benefits or limitations.
Agriculture is the efficient use of different technologies and methods to cultivate land, raise crops, and rear animals to produce food, fuel, fibre, and other necessities for human survival. The application of Unmanned Aerial Vehicle (UAV) technologies to smart farming transforms yield planning, precision spraying, and crop monitoring, resulting in improved resource management and higher production for farmers globally. There is a labor shortage as a consequence of the growing technology that has reduced the number of people working in agriculture. This issue can be resolved with the use of agricultural machinery, such as drones, to spray pesticides. Unfortunately, spraying pesticides in the mountainous orchards is difficult due to the environment, culture, and limitations in operation. The Bellman-Ford technique is used to update the distance estimates until resolution to find the optimal route in weighted elements. We suggest smart agriculture instructing drones to avoid obstructions and choose the most optimal routes for spraying pesticides in an approach that minimizes battery usage, pesticide costs, and operational challenges by fusing reinforcement learning (RL) using the Policy Gradient Method (PG) and the Gaussian filter using data preprocessing. This research conducted experiments using various reward systems, flying direction granularities, and settings to develop a strategy appropriate for slope orchards. When handling path planning problems in complicated contexts, RL works more accurately than the accurate in PG methods.
Firearm violence in educational settings represents a critical challenge to institutional security globally. This study proposes an innovative system for early detection of firearms using advanced computer vision techniques, convolutional neural networks (CNN), and computational intelligence in educational institutions. The main objective was to develop a deep learning model based on CNN architectures capable of identifying and alerting the presence of weapons in real time, improving traditional security protocols. The methodology implemented a systematic approach comprising three fundamental stages: data collection, model training, and experimental validation. For model training, a set of 9,000 rigorously labeled images processed using the Roboflow tool was used. The YOLOv5 algorithm, implemented in Google Colab, allows training a detection model with high-precision parameters. The system was integrated directly with the security camera infrastructure of the university campus. Experimental results demonstrated remarkable efficiency: 99% accuracy, 93% detection rate and 97% overall performance. The system can continuously monitor spaces, triggering instant alerts upon identification of potential threats, with a processing capacity that significantly exceeds traditional human monitoring. The findings suggest that machine vision is a promising strategy for strengthening preventive security in educational institutions. Future research will focus on optimizing the model's adaptability to diverse environments and environmental conditions, potentially expanding its implementation to different types of institutions.
The integration of AI with UAVs and IoT is prognosed as an innovative solution to set up communication networks in remote or rural areas that are not covered due to the absence of traditional infrastructure, aligning with "Industry, innovation and infrastructure" goals. This paper strategizes its plan in a way whereby UAVs dynamically manage resources for communication links between IoT sensors placed over a two-dimensional space. Advanced AI techniques significantly improve all available network parameters, like coverage, adaptability, and energy efficiency towards the achievement of reliable real-time communications. About 35% communication coverage improvement as well as 28% network adaptability for different scenarios together with about 20% lower energy consumption compared to standard UAV-based systems are some highlights of the results obtained through simulations. Emerging from these results is thereby underscored potential application interest that ranges from disaster management by precision agriculture to even environment monitoring support providing scalable yet resilient networks across disadvantaged regions.
An essential component of the Indian economy is agriculture. Precision farming, another call for smart farming, is a novel method of farming that incorporates modern innovations to improve production, sustainability, and performance over traditional techniques. These involve strong data security measurements, the reliance on high-quality sensor data, and possible integration issues with current farming systems. This study proposed a methodology for smart farming using IoT sensor statistics incorporated with a unique Dynamic Artificial Fish Swarm Driven XGBoost (DAFSXGBoost) model. Initially, research was conducted on a Kaggle dataset. Substantially, the "Crop recommender CSV" file, containing vital IoT sensor records, which include temperature, humidity, rainfall, soil potential of Hydrogen (pH), and nutrient ratios, changed into utilized. Data cleaning procedures were implemented to rectify inaccuracies, incompleteness, and redundancy. The dataset has been split into subsets for testing and training to improve the model. The proposed DAFSXGBoost approach, carried out with the use of MATLAB, is designed to enhance predictive accuracy while maintaining low latency and efficient hardware usage. The findings demonstrated that the proposed DAFS-XGBoost method achieves effective recall, accuracy, precision, f1-score, training time, and MSE rates as 93.76%, 96.73%, 94.61%, 90.12, 0.0006, and 0.19. The framework’s potential to combine outstanding performance and a hardware-friendly design to promote the longterm viability of smart farming is confirmed by simulations.
Recent research and studies focus on improving the accuracy of software project effort estimation by employing Agile methodology and machine learning and deep learning techniques, such as neural networks and convolutional networks. (CNN) and use of initial optimization techniques. Also relying on Story Points as a tool for estimating and estimating software effort, this research will utilize and study several recent studies related to the subject, and this study proposes an ensemblebased machine learning framework for accurately calculating the effort required to complete user stories in Agile software development. Effort estimation in Agile software development must not only focus on resource allocation but also on safeguarding sensitive data, ensuring system security throughout the software lifecycle. Textual data from user story titles are transformed into numerical vectors using advanced feature extraction techniques such as TF-IDF. The framework encompasses strong security practices, such as data encryption, access control, and identity management, to provide confidentiality and integrity of the data while the model is working. Multiple regression models (MLP, SVR, and Linear Regression) are used and combined through ensemble learning for improved prediction accuracy. Evaluation metrics (MAE, RMSE, SMAPE, and R²) are used to validate the usefulness of the model. Several security-enhanced metrics capitalize on contemporary machine learning methods to ensure privacy and trust of Agile teams with sensitive project data. The methodology allows Agile teams to automate story point estimation to improve sprint planning and resource allocation.
Forensic document authentication is used to identify fakes and verify police evidence and other legal documents, making detection and authentication part of forensic science. Existing approaches face issues about low generalizability and low interpretability of results, and inability to detect subtle amid manipulations or cross-modal inconsistencies. ForenXAI addresses this gap with a smart deep learning (DL) system that differentiates visual and textual forgeries through fused ResNet50-CBAM and LSTM with Attention. It cross-verifies using dual-stream cross-modal methods and performs risk assessment, SHAP-based interpretability, and XAI-derived probabilistic risk scoring. It structures the process within multi-stage pre-processing, image-text alignment, anomaly detection, and decision support to achieve legal accuracy. ForenXAI is optimized for system security and trust through real-time monitoring and evidence security, including auditable logs, encryption, and access control to forensic evidence and other sensitive data. Evaluation over multiple documents and signatures found the framework attains an accuracy of 0.9788 with MCC of 0.9576 and G-mean of 0.9788 at 70% training. This further improved to an accuracy of 0.9894 and MCC of 0.9788 at 80% training. Along with this, the framework attains an optimal F1-score of 0.9894. Comparative analysis across processing time, data precision, time taken to encrypt, delays in real time monitoring, and SHAP feature importance with Cycle-GAN, Ta-RNN, and NSVNN emphasizes ForenXAI’s efficiency and interpretability. These findings affirm that ForenXAI is principled, highly interpretable, secure, and extremely practical for forensic applications, thereby endorsing the use of AI systems in the policing and judiciary frameworks.
In recent times, the incorporation of EI into AI systems has increased user engagement, improved content moderation, and provided mental health support. This research proposes a security-based UEIM that uses multiple modalities to perform emotion recognition and behavioural trajectory analysis on social media platforms. Unlike conventional approaches that depend on only one source of input, UEIM leverages the capabilities of texts, images, and videos using a cross-modal attention fusion network known as CMAFNet, which helps secure the emotional data. To achieve accurate emotion trajectory analysis, Bi-LSTM networks have been used in the proposed approach. Graph attention networks (GATv2) have also been utilized to predict the spread of emotions among users. The UEIM can guarantee the privacy and security of the users’ data even when there is noise or missing data present, which is a frequent problem in real-life scenarios related to social media applications. From experimental results, the performance of the model is excellent since it obtained 88.2% F1-score in emotion recognition, 91.3% top-1 accuracy in emotion sequence prediction, and 85.2% micro-F1 for emotion propagation modelling. The MSE for emotion valence/arousal regression is 0.018. These results show the great accuracy of the model in emotion prediction and behaviour analysis, and this can be used in health monitoring and content moderation, among others.
This combination of philological and linguoculturological analysis is an extremely powerful tool for the analysis of the transfer, modification, and recontextualization of the idiomatic expressions in modern novels, especially in those that travel between linguistic and cultural spaces by means of digital networks, translation services, and the internet. In this paper the theoretical underpinnings of both fields are examined in a systematic way and it is suggested that the methodological integration of the two fields has shown to provide a better and more contextually informed understanding of idiomatic meaning making, than either approach is able to provide individually. This study is based on a corpus of 10 contemporary novels from the three language traditions of English, Arabic, and Russian to analyses the fate of culture-bound idiomatic units in the interlingual and intercultural transfer processes in the light of the Dual Philological-Linguocultural Model (DPLM). This study addresses two research questions: how etymological transparency and cultural connotation density affect idiomatic transfer, and how internet-mediated translation environments influence transfer outcomes. The function of the Internet is considered as a new mediator, such as the digital translation engines, online literary communities, and multilingual e-publishing platforms. A structured comparative framework and qualitative discourse analysis are applied to evaluate the transferability of the three categories of idioms: conceptual, somatic and historical-mythological. Quantitative analysis includes inter-annotator agreement testing and regression-based examination of the relationship between DPLM variables and transfer outcomes. Results validate the importance of the Linguacultural anchoring as significant predictor of difficulty in transfer and of philological etymological attention for its explanatory value. The paper presents a typological model of the transfer outcomes with five categories and presents suggestions for digital era translators, digital humanities scholars and NLP researchers.
In this paper, a new technique for storing medical images securely in cloud environments for telemedicine applications by implementing the CS-based method is suggested. The main problem here lies in the development of a compression algorithm that can provide minimal storage needs while maintaining the good quality of the images used for making diagnoses. The proposed method uses a combination of data encryption together with compression based on the CS approach, with the application of dictionary coding, sparse reconstruction, and lossy compression of images. The results of the experiments indicate substantial superiority of the suggested method over the conventional image compression algorithms like JPEG and JPEG2000, demonstrating compression 2 ratios (CR) between 1.166 and 1.703 and high peak signal-to-noise ratios (PSNR) compared to JPEG's 38.522 dB (40.322 dB). Furthermore, the method under consideration is characterized by high-security properties, which include NPCR = 99.8% and UACI = 34%. This guarantees the safety of encryption even in the case when keys differ from each other. Besides, the proposed technique proves to be quite efficient in terms of runtime, where the client side requires 120 ms to perform, while the cloud side needs 300 ms. The research findings prove that the suggested approach can be applied successfully in the field of telemedicine, providing an ideal trade-off between compressibility and security. Further work may involve improving the computational efficiency of the algorithm, along with widening its applicability to other medical imaging applications.
Peripheral Artery Disease (PAD) and chronic kidney disease (CKD) are highly correlated conditions of the vascular and metabolic systems, which cause high rates of morbidity and mortality across the globe. The identification of the early development of PAD to CKD is still a clinically complicated issue with complicated multimodal patterns of data and a late onset of symptoms. The proposed study is a multi-branch hybrid deep neural network that combines Xception, VGG19, and Efficient Net to predict chronic progression of PAD to CKD based on multimodal medical images and clinical data (CT imaging and MIMIC-III EHR dataset). These complementary CNN models can be fused to learn features, which can be further used to predict and learn features, and thus provide strong representation learning. Experimental analysis shows that the proposed model attains an accuracy of 99.55, precision of 99.65, recall of 99.50, and F1-score of 99.60, which is higher than individual CNN architectures and traditional deep learning models in various feature-selection strategies and validation splits. The framework is also computationally efficient; thus, it can be deployed in resource-constrained healthcare settings. Along with the high predictive accuracy of PAD to CKD development, the suggested multi-branch hybrid deep neural network (DNN) model is also concerned with the safe treatment of patient data. Secure data transmission protocols, encryption, and access control mechanisms are employed to guarantee the confidentiality and integrity of sensitive medical information. These security measures will comply with healthcare data protection requirements and adhere to privacy laws such as HIPAA. These findings substantiate the power of hybrid feature fusion and multi-branch learning in predicting early disease progression as a reliable and scalable AI-aided decision-support system to support clinical risk stratification and patient management.
The increase in digital technologies and the emergence of cloud-based literature libraries have contributed significantly to both the occurrence and threats associated with cross-language literary adaptation. As literary works transition across linguistic and cultural borders in an internet environment, there are two types of losses experienced simultaneously: semantic-cultural degradation, where cultural context is lost during the process of translation, and security concerns involving metadata, attribution of authors, provenance data, and digital rights management (DRM). This paper proposes a dual framework approach that assesses cultural context loss (CCL) in cross-language literary adaptation by applying the concepts of internet security in terms of data integrity, authenticity, non-repudiation, and access control. Using techniques and approaches from the fields of computational linguistics, digital humanities, and cybersecurity, this study examines how translation processes implemented using internet resources create vulnerability that is similar to man-in-the-middle (MitM) attacks, where both the cultural context and digital identity can be compromised. The Cultural Integrity and Security Model (CISM) is presented here as an all-encompassing assessment model. The efficacy of this model is tested using empirical analysis through the study of three different literary translation machines. In findings, the machine-assisted literary translations at a mean CCL rate of 34.7% show strong correlation (r = 0.71, p < 0.05) with the weakness of provenance authentication procedures. Five specific areas of security weakness have been identified, and recommendations have been made for securing literary translation processes.
Cloud-based healthcare records have contributed to making cloud storage practical for healthcare professionals. The main problem with cloud-based healthcare records is that they must be interpreted and transmitted over a network where sensitive health information is handled; so, the data must be preserved, be secure, and allow for reliable disease identification. A recommendation for cloud-based medical imaging analytics is a solution to the processing of healthcare records that integrates deep learning with privacy-aware cloud computing. The proposed healthcare record framework requires image normalization and enhancement prior to data encryption and cloud deployment. The recommended framework requires a pre-processing step prior to sending an image over the Internet, transmitting the pre-processed image, storing the pre-processed image in a secure environment, authenticating the streamer, demonstrating that the pre-processed image that was sent is still secure and valid through back verification of the original image and finally using a deep learning analytics engine to determine if there is a disease in the image has been completed. The image group from which the study was conducted consisted of 28500 images of multiple types of medical imaging. The Matthews correlation coefficient showed that the proposed framework produced superior diagnostic performance of 0.957, Jaccard index of 97.01%, balanced accuracy (overall sensitivity, specificity, and accuracy) of 98.08% unless stated otherwise, Cohen’s kappa of 0.954, area under the curve of 99.12%, and Diagnostic Reliability Index of 98.14%. Privacy ratio was 98.31%, data integrity was 98.56%, and access security was 98.72%. The implications of the above methods for the successful implementation of cloud solutions for the Medical Imaging Framework, as well as providing scalability and privacy provision for future integration of medical records using intelligent deep learning analysis, show that deep learning is the solution for the future of the medical imaging business in the modern healthcare sector.
