Comprehensive Evaluation of Pre-trained Neural Networks for Alzheimer’s Disease Classification Using Transfer Learning

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis for effective intervention. Recent advancements in deep learning and convolutional neural networks (CNNs) have shown significant potential in magnetic resonance imaging (MRI-based) AD classification. This study aims to evaluate the classification performance of three pre-trained CNN architectures, Visual Geometry Group 16-layer network (VGG16), densely connected convolutional network with 121 layers (DenseNet121), and residual neural network with 50 layers (ResNet50), in distinguishing AD stages using T1-weighted MRI data and transfer learning (TL). MRI scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative, covering five classes: cognitively normal, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD. After preprocessing and data augmentation, models were trained and validated using stratified fivefold cross-validation. VGG16 achieved the highest accuracy (95.0%), followed by DenseNet121 (90.4%) and ResNet50 (62.0%). Evaluation metrics included precision, recall, F1-score, confusion matrix, and receiver operating characteristic-area under the curve. The results support the effectiveness of TL for AD classification, particularly with VGG16. Future research may explore ensemble learning (e.g., VGG16 + DenseNet121), multimodal data fusion, and deployment in clinical settings

Exploring the Efficacy of Deep Learning Techniques in Detecting and Diagnosing Alzheimer’s Disease: A Comparative Study

Transfer learning has become extremely popular in recent years for tackling issues from various sectors, including the analysis of medical images. Medical image analysis has transformed medical care in recent years, enabling physicians to identify diseases early and accelerate patient recovery. Alzheimer’s disease (AD) diagnosis has been greatly aided by imaging. AD is a degenerative neurological condition that slowly deprives patients of their memory and cognitive abilities. Computed tomography (CT) and brain magnetic resonance imaging (MRI) scans are used to detect dementia in AD patients. This research primarily aims to classify AD patients into multiple classes using ResNet50, VGG16, and DenseNet121 as transfer learning along with convolutional neural networks on a large dataset as compared to existing approaches as it improves classification accuracy. The methods employed utilize CT and brain MRI scans for AD patient classification, considering various stages of AD. The study demonstrates promising results in predicting AD phases with MRI, yet challenges persist, including processing large datasets and cognitive workload involved in interpreting scans. Addressing image quality variations is crucial, necessitating advancements in imaging technology and analysis techniques. The different stages of AD are early mental retardation, mild mental impairment, late mild cognitive impairment, and final AD stage. The novel approach gives results with an accuracy of 96.6% and significantly improved outcomes compared to existing models

Exploring the Potential of Convolutional Neural Networks in Classifying Alzheimer’s Stages with Multi-biomarker Approach

Multiple studies have attempted to use a single type of data to predict various stages of Alzheimer’s disease (AD). However, combining multiple data modalities can improve prediction accuracy. In this study, we utilized a combination of biomarkers, including magnetic resonance imaging (MRI), electronic health records, and cerebrospinal fluid (CSF), to classify subjects into three groups based on clinical tests—normal cognitive controls (CN), mild cognitive impairment (MCI), and AD. To determine the significant parameters, we employ a novel technique that utilizes sparse autoencoders to extract features from CSF, clinical data, and convolutional neural networks’ (CNN’s) MRI imaging data. Our results indicate that deep learning methods outperform traditional machine learning models such as decision trees, support vector machines, random forests and K-nearest neighbors. The proposed method significantly outperforms traditional models, achieving an accuracy of 0.87 for CN versus AD, a precision of 0.93 for CN, and a recall of 0.88 for AD on the external test set. The integration of various data modalities and the application of deep learning techniques enhance the prediction accuracy, demonstrating the potential for improved diagnostic tools in clinical settings

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques

Introduction. Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions. Purpose. Stimulated by the increasing heart disease mortality rate incidents, an effective, low-cost, and reliable heart disease risk evaluation model is developed using significant non-invasive risk attributes. The significant non-invasive risk attributes like (age, systolic BP, diastolic BP, BMI, hereditary factor, smoking, alcohol, and physical inactivity) are identified by the help of medical domain experts, and their reliability in heart disease prediction is investigated through different feature selection techniques. Methodology. The enhancements of applying specific investigated techniques like random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor to the risk factors are tested. The heart disease risk assessment model is developed using the Jupyter Notebook web application, and its performance is tested not only through medical domain measures but also through the model performance measures. Findings. To evaluate heart disease risk evaluation model, we calculated measures of discrimination like error rate, AUROC, sensitivity, specificity, accuracy, precision, and so on. Experimental results show that the random forest heart disease risk evaluation model outperforms other existing risk models with admirable predictive accuracy and minimum misclassification rate. Originality. The heart disease risk evaluation model is developed based on novel non-invasive heart disease dataset, which consists of 5776 records. This dataset is collected from different heterogeneous data sources of Kashmir (India) through quantitative data collection methods. Research Implications. The risk model is applicable where people lack the facilities of integrated primary medical care technologies for untimely heart disease risk prediction. Future Work. To investigate deep learning and study the significance of other controlled attributes on different age and sex groups in the risk estimation of heart disease.</jats:p

An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning

Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an advanced lung carcinoma prediction and risk screening model using transfer learning is proposed. Our proposed model initially preprocesses lung computed tomography images for noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement. The next phase segments the preprocessed images using the modified Bates distribution coati optimization (B-RGS) algorithm to extract key features. The PResNet classifier then categorizes the cancer as normal or abnormal. For abnormal cases, further risk screening determines whether the risk is low or high. Experimental results depict that our proposed model performs at levels similar to other state-of-the-art models, achieving enhanced accuracy, precision, and recall rates of 98.21%, 98.71%, and 97.46%, respectively. These results validate the efficiency and effectiveness of our suggested methodology in early lung carcinoma prediction and risk assessment

Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients’ 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients’ lives

Impact of ICT in Modernizing the Global Education Industry to Yield Better Academic Outreach

The advancements made by information technology have redefined the concept, scope, and significance of communication. The barriers in the communication process have been wiped out by the recent advances in information and communication technology(ICT) backed by high-speed data connectivity. People are free to communicate without bothering about physical borders distancing them from one another. Information and communication technology has diversified its dynamism by creating an e-environment, where people exploit the power of technology and communication to deliver many services. This research used the conceptual framework for ICT-enabled learning management systems and described their dimensions and scope in ICT-enabled education. The ubiquity of ICT has revamped the education industry worldwide by introducing new approaches, tools, and techniques to modernize education. The widespread popularity of ICT has forced educational establishments to endorse this to update the academia to leverage its bounders and enhance productivity to yield productive outcomes at different levels of education. This paper describes different ICT approaches and investigates the importance, influence, and impact of ICT-enabled technologies on various educational practices to achieve productive educational outcomes. This research investigates the role of ICT in teaching and learning at different levels of education, explores various modulates and their influence on the overall development of educational activities, and identifies the research gaps that are bridged to achieve the primary aim of ICT and education. This research extended its ICT projections and scope to overcome the challenges emerging from pandemic circumstances and design and develop an online platform in proper consultation with market demand to make students more job-oriented or skill-oriented. This paper describes different ICT approaches adopted by various educational institutions across the globe to modernize student−teacher interaction. This paper further investigates the influence and impact of ICT-enabled technologies on various educational practices that are prerequisites for achieving productive educational outcomes.</jats:p

Impact of ICT in Modernizing the Global Education Industry to Yield Better Academic Outreach

The advancements made by information technology have redefined the concept, scope, and significance of communication. The barriers in the communication process have been wiped out by the recent advances in information and communication technology(ICT) backed by high-speed data connectivity. People are free to communicate without bothering about physical borders distancing them from one another. Information and communication technology has diversified its dynamism by creating an e-environment, where people exploit the power of technology and communication to deliver many services. This research used the conceptual framework for ICT-enabled learning management systems and described their dimensions and scope in ICT-enabled education. The ubiquity of ICT has revamped the education industry worldwide by introducing new approaches, tools, and techniques to modernize education. The widespread popularity of ICT has forced educational establishments to endorse this to update the academia to leverage its bounders and enhance productivity to yield productive outcomes at different levels of education. This paper describes different ICT approaches and investigates the importance, influence, and impact of ICT-enabled technologies on various educational practices to achieve productive educational outcomes. This research investigates the role of ICT in teaching and learning at different levels of education, explores various modulates and their influence on the overall development of educational activities, and identifies the research gaps that are bridged to achieve the primary aim of ICT and education. This research extended its ICT projections and scope to overcome the challenges emerging from pandemic circumstances and design and develop an online platform in proper consultation with market demand to make students more job-oriented or skill-oriented. This paper describes different ICT approaches adopted by various educational institutions across the globe to modernize student&minus;teacher interaction. This paper further investigates the influence and impact of ICT-enabled technologies on various educational practices that are prerequisites for achieving productive educational outcomes

Machine learning based intrusion detection framework for detecting security attacks in internet of things

Abstract The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models

Improved aquila optimizer for swarm-based solutions to complex engineering problems

Abstract The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO’s resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis