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Background/Objective: Breast cancer is a serious disease that has caused thousands of deaths around the world. According to the American Cancer Society, more than 40,000 women and about 600 men lost their lives due to breast cancer in 2021, and it increased to 43,700 women and 530 men until 2023. Method : In this paper, a modified version of ResNet-50 has been exploited to extract features from breast tissue biopsy slides, contained in the BreakHis public dataset. The standard 177 layer model is amended upto 146 layers by reducing redundant activation, normalization operations and number of convolutional filters without compromising representational capacity. As a result the computational efficiency is achieved along with reduction in learnable parameters from 23.7M to 16.8M. The features vector is extracted using novel Flea optimization Algorithm that performs exploration from a d-dimensional search space to get global features. An inter-image dissimilarity evaluation has been performed to find out class compactness and separation, demonstrating its crucial role in achieving better classification performance. The results of the proposed framework are obtained on various performance indicators including average accuracy, precision, recall, F1 score etc while the statistical analysis is made to see the reliability of the framework based on MCC, Cohen’s Kappa and t-test. Results: The results of the proposed method are compared with DenseNet, VGG, CNN with LSTM, Primal Dual Multi-instance SVM, Single Task CNN and Multi Task CNN and shown dominance on various performance measures. An accuracy of 99.20% was achieved at 40× magnification, 99.62% at 100× magnification, 99.50% at 200× magnification, 99.34% at 400× magnification, respectively. Conclusions: The proposed approach, implemented on the real hardware, can provide an alternate to health experts in diagnosing breast cancer in the early stages.

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user’s face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

The global education system comprises many technical and non-technical institutions. The selection of an institute plays a very important role in shaping the career of a student. With such a massive number of choices out there, the decision of which institution to go to will be a huge challenge for parents as well as students. Unexpected events such as the Covid-19 pandemic even disrupted global higher education highlighting infrastructural gaps and pedagogical limitations in knowledge delivery through sudden transitions to remote learning. Institutions were financially unstable with reductions in enrolment especially of international students. Increased operations cost for digital infrastructure and health protocols also took a toll on the academia, and it became important to predict the position of the institutions effectively. Our research proposes a fuzzy time series based ensemble model, ensemble based time series association (EBTsA) for dynamically predicting institutional rankings in the highly uncertain academic environment. The model integrates a fuzzy time series and ensemble machine learning algorithm for institutional rank prediction and capturing inherent variations induced by ranking uncertainties. It uses the method of fuzzification to adaptively consider the importance of rankings in a changing way over time, both before and after the pre- and post-COVID changes. This vital rank gap in earlier studies has personified rankings as static or uniform. Various algorithms such as FTS, FCA, IFS, IFS_New and the proposed algorithm (EBTsA) are compared based on their performance in the dynamic ranking prediction. The EBTsA model quantifies ranking uncertainties and forecasts institutional ranks with a mean absolute percentage error (MAPE) of 7.12, a mean absolute scaled error (MASE) of 0.32, and a directional accuracy (DA) of 82.2, outperforming conventional deterministic models. The predictive performance of the model ensures highly accurate and reliable dynamic rank forecasts, enabling stakeholders to make informed decisions about educational institutions. Our study may contribute to two sustainable development goals (SDGs)of the United Nations Organisation (UNO), such as (SDG 4), which provides “quality education and its connection to inequality”, and (SDG 10) for “reduced inequalities and its connection to education”.

Alzheimer’s disease (AD) is a neurodegenerative disease that affects a large number of people across the globe. Even though AD is one of the most commonly seen brain disorders, it is difficult to detect and it requires a categorical representation of features to differentiate similar patterns. Research into more complex problems, such as AD detection, frequently employs neural networks. Those approaches are regarded as well-understood and even sufficient by researchers and scientists without formal training in artificial intelligence. Thus, it is imperative to identify a method of detection that is fully automated and user-friendly to non-AI experts. The method should find efficient values for models’ design parameters promptly to simplify the neural network design process and subsequently democratize artificial intelligence. Further, multi-modal medical image fusion has richer modal features and a superior ability to represent information. A fusion image is formed by integrating relevant and complementary information from multiple input images to facilitate more accurate diagnosis and better treatment. This study presents a MultiAz-Net as a novel optimized ensemble-based deep neural network learning model that incorporate heterogeneous information from PET and MRI images to diagnose Alzheimer’s disease. Based on features extracted from the fused data, we propose an automated procedure for predicting the onset of AD at an early stage. Three steps are involved in the proposed architecture: image fusion, feature extraction, and classification. Additionally, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is presented as a multi-objective optimization algorithm to optimize the layers of the MultiAz-Net. The desired objective functions are imposed to achieve this, and the design parameters are searched for corresponding values. The proposed deep ensemble model has been tested to perform four Alzheimer’s disease categorization tasks, three binary categorizations, and one multi-class categorization task by utilizing the publicly available Alzheimer neuroimaging dataset. The proposed method achieved (92.3 ± 5.45)% accuracy for the multi-class-classification task, significantly better than other network models that have been reported.

Artificial intelligence (AI) and machine learning (ML) have made significant advances in the early detection and diagnosis of autism spectrum disorder (ASD), overcoming the limits of previous screening methods. These AI-based technologies offer more objective, scalable, and efficient methods for identifying risk behaviors associated with ASD. This article presents a novel approach for enhancing the detection and classification of ASD by integrating squeeze-and-excitation, multiscale attention mechanisms, and convolutional neural networks (CNNs) with automated hyperparameter optimization using the white shark optimization (WSO) algorithm. By leveraging attention mechanisms to focus on relevant facial features across multiple scales, this method enhances feature extraction, improves classification accuracy, and provides a robust framework for analyzing complex facial imaging data. An extensive autism dataset, encompassing both facial and multimodal datasets, was utilized in this study, including subjects from the non-ASD control (NC) group and individuals diagnosed with ASD. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art methods, achieving a high accuracy of 95.36%, precision of 92.62%, and an F1-score of 95.5% for ASD detection and classification. This proposed model is a promising tool for the accurate and early identification of ASD, which is crucial for effective treatment and management. By providing deeper insights into distinctive facial patterns and morphological features associated with ASD, the model enables physicians to make more informed decisions and develop targeted treatment plans, ultimately improving patient outcomes.

There are several major threats to crop production. As herbicide use has become overly reliant on weed control, herbicide-resistant weeds have evolved and pose an increasing threat to the environment, food safety, and human health. Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of images for the identification of weeds from crop images that are captured by drones. Manually designing such neural architectures is, however, an error-prone and time-consuming process. Natural-inspired optimization algorithms have been widely used to design and optimize neural networks, since they can perform a blackbox optimization process without explicitly formulating mathematical formulations or providing gradient information to develop appropriate representations and search paradigms for solutions. Harris Hawk Optimization algorithms (HHO) have been developed in recent years to identify optimal or near-optimal solutions to difficult problems automatically, thus overcoming the limitations of human judgment. A new automated architecture based on DenseNet-121 and DenseNet-201 models is presented in this study, which is called “DenseHHO”. A novel CNN architecture design is devised to classify weed images captured by sprayer drones using the Harris Hawk Optimization algorithm (HHO) by selecting the most appropriate parameters. Based on the results of this study, the proposed method is capable of detecting weeds in unstructured field environments with an average accuracy of 98.44% using DenseNet-121 and 97.91% using DenseNet-201, the highest accuracy among optimization-based weed-detection strategies.

Background Metabolic dysfunction and cachexia are associated with poor cancer prognosis. With no pharmacological treatments, it is crucial to define the molecular mechanisms causing cancer‐induced metabolic dysfunction and cachexia. Adenosine monophosphate‐activated protein kinase (AMPK) connects metabolic and muscle mass regulation. As AMPK could be a potential treatment target, it is important to determine the function for AMPK in cancer‐associated metabolic dysfunction and cachexia. We therefore established AMPK's roles in cancer‐associated metabolic dysfunction, insulin resistance and cachexia. Methods In vastus lateralis muscle biopsies from n = 26 patients with non‐small cell lung cancer (NSCLC), AMPK signalling and protein content were examined by immunoblotting. To determine the role of muscle AMPK, male mice overexpressing a dominant‐negative AMPKα2 (kinase‐dead [KiDe]) specifically in striated muscle were inoculated with Lewis lung carcinoma (LLC) cells (wild type [WT]: n = 27, WT + LLC: n = 34, mAMPK‐KiDe: n = 23, mAMPK‐KiDe + LLC: n = 38). Moreover, male LLC‐tumour‐bearing mice were treated with (n = 10)/without (n = 9) 5‐aminoimidazole‐4‐carboxamide ribonucleotide (AICAR) to activate AMPK for 13 days. Littermate mice were used as controls. Metabolic phenotyping of mice was performed via indirect calorimetry, body composition analyses, glucose and insulin tolerance tests, tissue‐specific 2‐[3H]deoxy‐d‐glucose (2‐DG) uptake and immunoblotting. Results Patients with NSCLC presented increased muscle protein content of AMPK subunits α1, α2, β2, γ1 and γ3 ranging from +27% to +79% compared with control subjects. In patients with NSCLC, AMPK subunit protein content correlated with weight loss (α1, α2, β2 and γ1), fat‐free mass (α1, β2 and γ1) and fat mass (α1 and γ1). Tumour‐bearing mAMPK‐KiDe mice presented increased fat loss and glucose and insulin intolerance. LLC in mAMPK‐KiDe mice displayed lower insulin‐stimulated 2‐DG uptake in skeletal muscle (quadriceps: −35%, soleus: −49%, extensor digitorum longus: −48%) and the heart (−29%) than that in non‐tumour‐bearing mice. In skeletal muscle, mAMPK‐KiDe abrogated the tumour‐induced increase in insulin‐stimulated TBC1D4thr642 phosphorylation. The protein content of TBC1D4 (+26%), pyruvate dehydrogenase (PDH; +94%), PDH kinases (+45% to +100%) and glycogen synthase (+48%) was increased in skeletal muscle of tumour‐bearing mice in an AMPK‐dependent manner. Lastly, chronic AICAR treatment elevated hexokinase II protein content and normalized phosphorylation of p70S6Kthr389 (mTORC1 substrate) and ACCser212 (AMPK substrate) and rescued cancer‐induced insulin intolerance. Conclusions Protein contents of AMPK subunits were upregulated in skeletal muscle of patients with NSCLC. AMPK activation seemed protectively inferred by AMPK‐deficient mice developing metabolic dysfunction in response to cancer, including AMPK‐dependent regulation of multiple proteins crucial for glucose metabolism. These observations highlight the potential for targeting AMPK to counter cancer‐associated metabolic dysfunction and possibly cachexia.

Workers on construction sites face numerous health and safety risks. Authorities have made numerous attempts to enhance safety management; yet incidents continue to occur, impacting both worker health and the project’s forward momentum. To that end, developing strategies to improve construction site safety management is crucial. The goal of this project is to employ computer vision and deep learning methods to create a model that can recognize construction workers, their PPE and the surrounding heavy equipment from CCTV footage. Then, the hazards can be discovered and identified based on an analysis of the imagery data and other criteria including weather conditions, and the on-site safety officer can be contacted. Our own dataset was used to train the You Only Look Once model, version 5 (YOLO-v5), which was put to use as an object detection model. The detection model’s performance in tests showed promise for fast and accurate object recognition in the field.

The various corn diseases that affect agriculture go unnoticed by farmers. Each day, more crops fail due to diseases as there is no effective treatment or a way to identify the illness. Common rust, blight, and the northern leaf grey spot are the most prevalent corn diseases. The presence of a disease cannot be accurately detected by simply looking at the plant. This will lead to improper pesticide use, which harms people by bringing on chronic diseases. Therefore, maintaining food security depends on accurate and automatic disease detection. It might be possible to save time and stop crop degradation before it takes place by utilising digital technologies. Hence, applying modern digital technologies to identify the disease in the damaged corn fields automatically will be more advantageous to the farmers. Many academics have recently become interested in deep learning, which has aided in creating an exact and autonomous picture classification scheme. The use of deep learning techniques and their adjustments for detecting corn illnesses can greatly assist contemporary agriculture. To find plant leaf diseases, we employ image acquisition, preprocessing, and classification processes. Preprocessing includes procedures such as reading images, resizing images, and data augmentation. The suggested project is based on EfficientNet and improves the precision of the database of corn leaf diseases by tweaking the variables. Tests are run using DenseNet and Resnet on the test dataset to confirm the precision and robustness of this approach. The recognition accuracy of 98.85% that can be achieved using this method, according to experimental results, is significantly higher than those of other cutting-edge techniques.

Alzheimer’s disease (AD) is a neurological disease that affects numerous people. The condition causes brain atrophy, which leads to memory loss, cognitive impairment, and death. In its early stages, Alzheimer’s disease is tricky to predict. Therefore, treatment provided at an early stage of AD is more effective and causes less damage than treatment at a later stage. Although AD is a common brain condition, it is difficult to recognize, and its classification requires a discriminative feature representation to separate similar brain patterns. Multimodal neuroimage information that combines multiple medical images can classify and diagnose AD more accurately and comprehensively. Magnetic resonance imaging (MRI) has been used for decades to assist physicians in diagnosing Alzheimer’s disease. Deep models have detected AD with high accuracy in computing-assisted imaging and diagnosis by minimizing the need for hand-crafted feature extraction from MRI images. This study proposes a multimodal image fusion method to fuse MRI neuroimages with a modular set of image preprocessing procedures to automatically fuse and convert Alzheimer’s disease neuroimaging initiative (ADNI) into the BIDS standard for classifying different MRI data of Alzheimer’s subjects from normal controls. Furthermore, a 3D convolutional neural network is used to learn generic features by capturing AlD biomarkers in the fused images, resulting in richer multimodal feature information. Finally, a conventional CNN with three classifiers, including Softmax, SVM, and RF, forecasts and classifies the extracted Alzheimer’s brain multimodal traits from a normal healthy brain. The findings reveal that the proposed method can efficiently predict AD progression by combining high-dimensional MRI characteristics from different public sources with an accuracy range from 88.7% to 99% and outperforming baseline models when applied to MRI-derived voxel features.