Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network.

Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.

PulmoNet: a novel deep learning based pulmonary diseases detection model.

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.

Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.

A hybrid deep CNN model for brain tumor image multi-classification.

The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks. The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into their different grades. To ensure optimal performance, a grid search optimization approach is employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The utilization of large, publicly accessible clinical datasets results in robust and reliable classification outcomes. This article conducts a comprehensive comparison of the proposed models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the field of brain tumor classification and early detection.

Piperine-coated zinc oxide nanoparticles target biofilms and induce oral cancer apoptosis via BCl-2/BAX/P53 pathway.

BACKGROUND: Dental pathogens play a crucial role in oral health issues, including tooth decay, gum disease, and oral infections, and recent research suggests a link between these pathogens and oral cancer initiation and progression. Innovative therapeutic approaches are needed due to antibiotic resistance concerns and treatment limitations. METHODS: We synthesized and analyzed piperine-coated zinc oxide nanoparticles (ZnO-PIP NPs) using UV spectroscopy, SEM, XRD, FTIR, and EDAX. Antioxidant and antimicrobial effectiveness were evaluated through DPPH, ABTS, and MIC assays, while the anticancer properties were assessed on KB oral squamous carcinoma cells. RESULTS: ZnO-PIP NPs exhibited significant antioxidant activity and a MIC of 50 µg/mL against dental pathogens, indicating strong antimicrobial properties. Interaction analysis revealed high binding affinity with dental pathogens. ZnO-PIP NPs showed dose-dependent anticancer activity on KB cells, upregulating apoptotic genes BCL2, BAX, and P53. CONCLUSIONS: This approach offers a multifaceted solution to combatting both oral infections and cancer, showcasing their potential for significant advancement in oral healthcare. It is essential to acknowledge potential limitations and challenges associated with the use of ZnO NPs in clinical applications. These may include concerns regarding nanoparticle toxicity, biocompatibility, and long-term safety. Further research and rigorous testing are warranted to address these issues and ensure the safe and effective translation of ZnO-PIP NPs into clinical practice.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Termites as human foods-A comprehensive review.

Global food production is anticipated to rise along with the growth of the global population. As a result, creative solutions must be devised to ensure that everyone has access to nutritious, affordable, and safe food. Consequently, including insects in diets has the potential to improve global food and nutrition security. This paper aims to share recent findings by covering edible termites as the main aspect, from their consumption record until consumer acceptance. A total of 53 termite species are reported as edible ones and distributed in 6 biogeographic realms. Generally, termites have a nutrient composition that is suitable for human consumption, and cooked termites are a better dietary choice than their raw counterparts. Besides, increasing customer interest in eating termite-based food can be achieved by making it more palatable and tastier through various cooking processes, that is, boiling, frying, grilling, roasting, smoking, and sun-drying. Moreover, edible termites can also be used as a new source of medication by exhibiting antimicrobial activity. Regarding their advantages, it is strongly encouraged to implement a seminatural rearing system to sustain the supply of edible termites. Overall, this paper makes it evident that termites are an important natural resource for food or medicine. Hence, the long-term objective is to stimulate scientific inquiry into the potential of edible insects as an answer to the problem of global food security.

Legal situation and consumer acceptance of insects being eaten as human food in different nations across the world-A comprehensive review.

Insect consumption is a traditional practice in many countries. Currently, the urgent need for ensuring food sustainability and the high pressure from degrading environment are urging food scientists to rethink the possibility of introducing edible insects as a promising food type. However, due to the lack of the standardized legislative rules and the adequate scientific data that demonstrate the safety of edible insects, many countries still consider it a grey area to introduce edible insects into food supply chains. In this review, we comprehensively reviewed the legal situation, consumer willingness, acceptance, and the knowledge on edible insect harvesting, processing as well as their safety concerns. We found that, despite the great advantage of introducing edible insects in food supply chains, the legal situation and consumer acceptance for edible insects are still unsatisfactory and vary considerably in different countries, which mostly depend on geographical locations and cultural backgrounds involving psychological, social, religious, and anthropological factors. Besides, the safety concern of edible insect consumption is still a major issue hurdling the promotion of edible insects, which is particularly concerning for countries with no practice in consuming insects. Fortunately, the situation is improving. So far, some commercial insect products like energy bars, burgers, and snack foods have emerged in the market. Furthermore, the European Union has also recently issued a specific item for regulating new foods, which is believed to establish an authorized procedure to promote insect-based foods and should be an important step for marketizing edible insects in the near future.

Effects of undesired substances and their bioaccumulation on the black soldier fly larvae, Hermetia illucens (Diptera: Stratiomyidae)-a literature review.

Black soldier fly (BSF), Hermetia illucens (L.) (Diptera: Stratiomyidae), is predominantly reared on organic wastes and other unused complementary substrates. However, BSF may have a buildup of undesired substances in their body. The contamination of undesired substance, e.g., heavy metals, mycotoxins, and pesticides, in BSF mainly occurred during the feeding process in the larval stage. Yet, the pattern of accumulated contaminants in the bodies of BSF larvae (BSFL) is varied distinctively depending on the diets as well as the contaminant types and concentrations. Heavy metals, including cadmium, copper, arsenic, and lead, were reported to have accumulated in BSFL. In most cases, the cadmium, arsenic, and lead concentration in BSFL exceeded the recommended standard for heavy metals occurring in feed and food. Following the results concerning the accumulation of the undesired substance in BSFL's body, they did not affect the biological parameters of BSFL, unless the amounts of heavy metals in their diets are highly exceeding their thresholds. Meanwhile, a study on the fate of pesticides and mycotoxins in BSFL indicates that no bioaccumulation was detected for any of the target substances. In addition, dioxins, PCBs, PAHs, and pharmaceuticals did not accumulate in BSFL in the few existing studies. However, future studies are needed to assess the long-term effects of the aforementioned undesired substances on the demographic traits of BSF and to develop appropriate waste management technology. Since the end products of BSFL that are contaminated pose a threat to both human and animal health, their nutrition and production process must be well managed to create end products with a low contamination level to achieve a closed food cycle of BSF as animal feed.

Entropy for q-rung linear diophantine fuzzy hypersoft set with its application in MADM.

A notable advancement in fuzzy set theory is the q-rung linear diophantine fuzzy set. The soft set theory was expanded into the hypersoft set theory. By combining both the q-rung linear diophantine fuzzy set and hypersoft set, this study describes the notion of q-rung linear diophantine fuzzy hypersoft set that can handle multi sub-attributed q-rung linear diophantine fuzzy situations in the real world. Furthermore, some of its algebraic operations such as union, intersection and complement are described in this study. In addtion, the entropy measure of the q-rung linear diophantine fuzzy hypersoft set is established as it is helpful in determining the degree of fuzziness of q-rung linear diophantine fuzzy hypersoft sets. A multi-attribute decision making algorithm based on suggested entropy is presented in this study along with a numerical example of selecting a suitable wastewater treatment technology to demonstrate the effectiveness of the proposed algorithm in real-life situations. A comparative study was undertaken that describes the validity, robustness and superiority of the proposed algorithm and notions by discussing the advantages and drawbacks of existing theories and algorithms. Overall, this study describes a novel fuzzy extension that prevails over the existing ones and contributes to the real world with a valid real-life multi-attribute decision making algorithm that can cover many real-world problems that are unable to be addressed by the existing methodology.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications.

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

A robust self-supervised approach for fine-grained crack detection in concrete structures.

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

Royal Poinciana Biodiesel Blends with 1-Butanol as a Potential Alternative Fuel for Unmodified Research Engines.

This research work investigates the experimental work of a single-cylinder diesel engine operated with royal poinciana biodiesel blends with various proportions of 10, 20, and 30% volume with 1-butanol as an effective ignition-improving additive. The test blends were indicated as D90RP7B3 (90% diesel + 7% royal poinciana biodiesel + 3% butanol), D80RP14B6 (80% diesel + 14% royal poinciana biodiesel + 6% butanol), D70RP21B9 (70% diesel + 21% royal poinciana biodiesel + 9% butanol), and pure royal poinciana biodiesel (RP100) and diesel. The significant findings or results obtained during the experimentation are that BTE is suitable for blend D90RP7B3, and the least BSFC is found for blend D90RP7B3 in the 0.24 kg/kWh range. The inline cylinder pressures are found to be suitable for the blend D90RP7B3 in the range of 7 MPa; HRR is ideal for both the blends D90RP7B3 and D80RP14B6 in the range of 90 and 88 kJ; D90RP7B3 possesses adequate ignition delay at full load conditions 16° in crank angle advance; maximum A/F ratios are well suitable for the blend D90RP7B3 in the ratio 11:1 at higher loads. Volumetric efficiency is achieved well for all the blends and diesel; the emissions released from the royal poinciana blends, such as CO, CO2, HC, and NOX, were reduced by 14.12, 8.33, 11.1, and 18.8% compared to standard diesel. Hence, royal poinciana blends with 1-butanol can be considered the best fuels in the automobile sector.

Impact of low-oxalate diet on hyperoxaluria among patients suffering from nephrolithiasis.

Low-oxalate diets are useful for treating hyperoxaluria in nephrolithiasis patients. This study was unique in examining how a low-oxalate diet in addition to a standard diet affected hyperoxaluria and renal function tests in nephrolithiasis patients. The effects of a low-oxalate diet were analyzed by different biochemical tests, that is, anthropometric measurements, blood oxalate test, renal function test, electrolyte profile test, and 24 h urine analysis. For this purpose, 112 patients were divided into 2 groups: Group T1 (Conventional diet) and Group T2 (Low-Oxalate diet) for 8 weeks. Each group was tested at the initiation and end of the study. Using SPSS, the obtained data from each parameter were statistically analyzed. The results showed that a low-oxalate diet had a positive effect on patients suffering from nephrolithiasis. Furthermore, after treatment, anthropometric measurement weight (kg) among the control group (T1) was 100.45 ± 5.65 and the treatment group (T2) was 79.71 ± 9.48 kg. The effect of low-oxalate diet on renal function test: creatinine (g/d) among T1 was 2.08 ± 0.86 and T2 was 1.17 ± 0.13, uric acid(mg/d) among T1 was 437.04 ± 24.20 and T2 was 364.61 ± 35.99, urinary oxalate (mg/d) among T1 was 76.84 ± 10.33 and T2 was 39.24 ± 1.51, respectively. Sodium (mEq/d) among T1 was 156.72 ± 6.37 and T2 was 159.84 ± 6.31, potassium (mEq/d) among T1 was 69.91 ± 15.37 and T2 was 89.21 ± 6.31, phosphorus (g/d) among T1 was 0.96 ± 0.07 and T2 was 0.34 ± 0.27, respectively. This study demonstrated that nephrolithiasis patients with hyperoxaluria benefit from low-oxalate diets. Hyperoxaluria patients should eat a low-oxalate diet to use oxalate without affecting metabolism and eliminate it from the kidney without stones.

The mediating role of leader-member exchange (LMX) in the structural relationship between organizational justice and employee voice behaviour in higher education.

Within the vibrant yet complex ecosystem of higher education institutions, nurturing a culture of employee voice is critical for driving innovation, fostering engagement, and optimizing decision-making. However, it's still a complex task to identify the main factors influencing voice behaviour. This study ventures into this fertile ground, examining the mediating role of leader-member exchanges in the structural relationship between perceived organizational justice and employee voice behaviour in higher education. A correlational study design was used. A total of 361 participants were involved in the study. Data were collected using a questionnaire and analyzed using structural education modelling (SEM). The study found that both perceived organizational justice and leader-member exchange have a significant direct influence on employee voice behaviour, suggesting that both variables are important factors in predicting employee voice behaviour. Despite these effects, the contribution of leader-member exchange was found to be more impactful than perceived organizational justice. When the leader-member exchange is entered as a mediating variable in the structural model, the indirect effect of organizational justice becomes large. This suggests that when employees perceive their organization as fair, they are more likely to form positive relationships with their leaders and, as a result, these relationships lead to greater employee voice behaviour. Therefore, it can be concluded that higher education institutions are recommended to create a fair and equitable working environment strengthened by strong relationships between leaders and employees, as this directly contributes to improving employees' ability to express their thoughts and opinions for the benefit of the institutions.

Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.

In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP's efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.

Employing deep learning and transfer learning for accurate brain tumor detection.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Trained neural networking framework based skin cancer diagnosis and categorization using grey wolf optimization.

Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.