efficientnetb7-based-deep-learning-framework-for-enhanced

EfficientNetB7-Based Deep Learning Framework for Enhanced Classification of Lung and Colon Cancer Histopathological Images

Early diagnosis of lung cancer plays a pivotal role in ensuring improved treatment and survival of patients. This remains a major focus in clinical ...

Princess Nourah bint Abdulrahman University

RETRACTED ARTICLE: Internet of Things Based Intelligent Transportation of Food Products During COVID.

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN.

In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm.

Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

A Proposed Framework for Early Prediction of Schistosomiasis.

Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry.

A robust NIfTI image authentication framework to ensure reliable and safe diagnosis.

In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal-dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant's private medical information.

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records.

The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network-Capsule Network (CapsNet)-and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model's remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma.

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance.

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers.

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user's identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.

A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration.

Brain tumor categorization on MRI is a challenging but crucial task in medical imaging, requiring high resilience and accuracy for effective diagnostic applications. This study describe a unique multimodal scheme combining the capabilities of deep learning with ensemble learning approaches to overcome these issues.

Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM.

This paper presents AquaFlowNet, a machine learning-based algorithm for real-time wastewater flow management. It addresses critical challenges related to operational efficiency, resource optimization, and environmental sustainability. Wastewater management systems require innovative methods for dynamic and efficient flow control to meet growing demands driven by urbanization, climate change, and increasingly stringent regulations. However, most existing methods rely on static or rule-based models, which lack the flexibility to handle fluctuating flow rates, variable environmental loads, and unforeseen disruptions. These limitations often lead to inefficiencies such as energy wastage, treatment delays, and overflow incidents, negatively impacting system performance and sustainability.AquaFlowNet leverages state-of-the-art machine learning algorithms to analyze real-time data from sensors, forecast flow variations, and optimize wastewater treatment processes. By integrating predictive analytics with intelligent control strategies, it enhances resource efficiency, prevents overflow events, and ensures regulatory compliance. Experimental evaluations demonstrate that AquaFlowNet outperforms conventional approaches in prediction accuracy and operational efficiency, reducing energy consumption, improving treatment effectiveness, and mitigating environmental impacts.The results highlight AquaFlowNet's potential to revolutionize wastewater management systems, making them more resilient, adaptive, and beneficial for urban and industrial applications.

AquaFlowNet a machine learning based framework for real time wastewater flow management and optimization.

Water is one of the most critical and finite resources on our planet. As the demand for freshwater continues to grow, effectively managing and purifying existing water sources becomes increasingly important. This study introduces a Machine learning-based approach for enhancing water quality monitoring and anomaly detection in treatment plants using a modified Quality Index (QI). The proposed method integrates an encoder-decoder architecture with real-time anomaly detection and adaptive QI computation, providing a dynamic evaluation of water quality. In addition to developing this model, we present a comparative analysis with several existing machine learning models, demonstrating the effectiveness of our approach in detecting water quality anomalies. The revised QI is continuously updated using real-time sensor data, aiding decision-making in treatment operations. Experimental results show that the proposed model achieves superior performance, with an accuracy of 89.18%, precision of 85.54%, recall of 94.02%, Critical Success Index of 93.42%, Matthews Correlation Coefficient of 88.40%, delta-P of 94.37%, and Fowlkes-Mallow's Index of 89.47%. These results highlight the model's strong predictive capability and its practical utility in improving water treatment plant efficiency. By combining Machine learning with adaptive quality assessment, this study contributes to advancing intelligent monitoring solutions in water management.

Comparative analysis of machine learning models for detecting water quality anomalies in treatment plants.

Cardiovascular diseases (CVDs) are still the leading cause of death in the worldwide. Traditional machine learning models often have difficulty in determine how to capture the complex links between disease risk factors and disease occurrence. This article discusses a hybrid machine learning approach for cardiovascular risk prediction (HMLCRP) to address this problem. This approach combines logistic regression (LR), support vector machines (SVMs) and neural networks (NNs) to make predictions more correct and reliable. The proposed model looks at important coronary heart sickness risk factors, including excessive blood pressure, a record of coronary heart disorder within the family, pressure, age, sex, levels of cholesterol, body mass index (BMI) and poor dwelling choices. The hybrid technique makes use of the nice functions of LR for clean understanding, SVM for dealing with large amounts of facts and NNs for finding developments. By integrating these models together, the HMLCRP makes positive that type is correct and that danger predictions are accurate. In this study, benchmark datasets used, which include the cardio statistics set, heart ailment dataset and Framingham heart examination dataset, are used to train and test the version. Popular parameter measures, such as accuracy, precision, recall and the F1-score, are used to determine overall performance. The results of the experiments indicate that the HMLCRP is better at predicting effects than individual models. The suggested combination model is a major step forward in personalised healthcare because it allows proactive risk management and early intervention methods to stop CVD.

Article	Year
RETRACTED ARTICLE: Internet of Things Based Intelligent Transportation of Food Products During COVID. Wireless personal communications\| PubMed ID: 34456512	2022
Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN. Computational intelligence and neuroscience\| PubMed ID: 35789609	2022
A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. Diagnostics (Basel, Switzerland)\| PubMed ID: 36359487	2022
A Proposed Framework for Early Prediction of Schistosomiasis. Diagnostics (Basel, Switzerland)\| PubMed ID: 36553145	2022
A robust NIfTI image authentication framework to ensure reliable and safe diagnosis. PeerJ. Computer science\| PubMed ID: 37346677	2023
FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records. Diagnostics (Basel, Switzerland)\| PubMed ID: 37891987	2023
Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants. Diagnostics (Basel, Switzerland)\| PubMed ID: 37998555	2023
A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics (Basel, Switzerland)\| PubMed ID: 37998597	2023
Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers. PloS one\| PubMed ID: 38527047	2024
A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration. Sensors (Basel, Switzerland)\| PubMed ID: 39517989	2024
Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM. Frontiers in medicine\| PubMed ID: 40276738	2025
AquaFlowNet a machine learning based framework for real time wastewater flow management and optimization. Scientific reports\| PubMed ID: 40450065	2025
Comparative analysis of machine learning models for detecting water quality anomalies in treatment plants. Scientific reports\| PubMed ID: 40830563	2025
A Unified Hybrid Model for Cardiovascular Risk Prediction: Merging Statistical, Kernel-Based and Neural Approaches. Journal of cellular and molecular medicine\| PubMed ID: 40874542	2025
Attention driven deep convolutional network with optimized learning for accurate landslide detection and monitoring. Scientific reports\| PubMed ID: 41617912	2026

Shakila Basheer

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