People throughout the world are exposed to different levels of climate changes (CCs) owing to global warming. CCs are important disregarded subjects considering them as one of the global risks in the 21st century, according to the United Nations Human Development Report, 2019. Hence, studies are concentrating on re-evaluating long-term growths to combat CCs and build a sustainable future where the earth can be assessed, restored, and protected. The employment of Deep Learning (DL) approaches offers previously unheard-of chances to turn massive amounts of unstructured data into useful information. Climate policymakers and other stakeholders can benefit from analysis if CCs data using DL approaches. This study analyzes CCs using a DL framework with the goal of identifying common parameters and data sources related to CCs. This work evaluates global changes in climate using a DL-based framework called CCALR (climatic change assessments using Linear Regression) and offers solutions based on SDG parameters. The main objective of this work is to leverage information from existing worldwide statistics to identify practical parameters that may be used too address environmental issues.

Corn leaf disease prediction is essential in ensuring agricultural productivity and food security. Although classical segmentation and classification techniques have been used to identify diseases, they often fail to deal with complex leaf structures. Thus, deep learning has attracted growing attention for automatically learning discriminative features from data. This paper proposes a new approach to predicting corn leaf disease using deep learning and classical segmentation techniques for improved accuracy and efficiency. The proposed work presents an adaptive colour edge segmentation method improved by a hill-climbing search for robust feature extraction according to the diversified nature of corn leaves. With the help of stochastic hill climbing, the segmentation process is optimized by adaptively changing parameters to get better segmentation accuracy. After that, features are extracted and provided as input into a ResNeXt-101 model for disease classification. The novel activation function in ResNeXt-101 combines the Adam optimizer with a Gaussian-based GELU to enhance nonlinearity to the smooth activation of GELU. The results obtained from experiments show that the proposed approach is practical. The ResNeXt-101 model has achieved overwhelming performance, with 99

This chapter explores the innovative use of nanosensors for precision monitoring and analysis of soil health and environmental conditions. Nanotechnology offers unprecedented opportunities to develop sensors capable of detecting and quantifying various soil parameters with high sensitivity and specificity. By leveraging nanomaterials and nanotechnology principles, these sensors can provide real-time data on soil moisture, pH levels, nutrient concentrations, contaminant presence, and microbial activity, among other critical factors. This chapter discusses the principles underlying nanosensor design, fabrication, and deployment in soil environments. It explores the diverse applications of nanosensors in precision agriculture, environmental monitoring, and soil remediation efforts. Furthermore, the chapter examines the challenges and future prospects of integrating nanosensor technologies into soil science practices, emphasizing their potential to revolutionize soil management strategies and promote sustainable agriculture and environmental stewardship.

Stroke is still a leading cause of death and long-term disability worldwide, and therefore, early and accurate prediction is required for immediate intervention. This paper provides a comparative analysis of three supervised Learning models, Logistics Regression (LR), Decision Tree (DT) and Random Forest (RF) for stroke prediction using an open accessed healthcare dataset. After preprocessing involving missing value imputation, label encoding, feature scaling and balancing the classes by SMOTE, each model was fitted and evaluated on stratified dataset. The models were assessed through basic classification indicators including accuracy, precision, recall and F1-score. The Logistic Regression model achieved a fair accuracy (79.18%) and was chosen based on its interpretability. The Decision Tree model yielded good accuracy (89.46%) with improved sensitivity and well-balanced precision-recall characteristics. The Random Forest's test accuracy was the highest of all three models at 95.01%, demonstrating that it effectively reduced the false positive and negative rates in ensemble learning. Additionally, the confusion matrices confirmed the capability of Random Forest to properly recognize cases of stroke at a low rate of misclassification. The graphing of model metrics using comparison bar plots illustrated the best performance of Random Forest in comparison to other evaluation metrics. Logistic Regression provides interpretability and efficiency, Decision Tree provides a clear decision rule, and Random Forest is the most accurate and reliable for practical clinical use.

Fetal health monitoring is important in reducing newborn and maternal mortality, and such efforts are key targets in the UN's Sustainable Development Goals. Maternal deaths, which are largely avoidable, continue at unacceptable levels especially in resource constrained settings. Non-invasive CTGs assess the fetal heart rate and uterine contractions 60 and provide a cost-effective opportunity to evaluate fetal health second-by-second. Application of such information and machine learning can identify fetal distress at an early stage, and subsequently early therapeutic measures can be implemented. In this work, the author investigates the segmentation of fetal health status with dataset of 2,126 CTG records labeled by expert obstetricians in three categories including Normal, Suspect and Pathological. Each case is a set of numeric attributes that are characteristic of CTG records, which describe the health of a fetus. We design and evaluate several machine learning techniques to automatic classification of fetal well-being among pregnant women using machine learning algorithms. The stratified dataset with a set predefined features is used to train and evaluate the models to ensure strong performance, which in available public in Kaggle. The evaluation measures include accuracy, precision, recall, F1score, and confusion matrix analysis for assessing classification performance of the algorithms. Some initial results show that machine learning methods including Random Forest are better at differentiating all three prenatal health conditions. These findings highlight the potential role of AI-based decision support tools to support medical experts in maternal health care, especially in areas with inadequate access to expert review in maternal healthcare. The code is available on the GitHub repository https://github.com/adstechlearning/Fetal-MLAlgorithms.

Properly reading subtle emotion in online text, especially the phrases that imply sarcasm, irony, and hidden emotions, is a major and increasingly difficult task of an automated system. To overcome this shortcoming, this study proposes a new hybrid architecture that combines Bidirectional Encoder Representations of Transformers (BERT) and Convolutional Neural Networks (CNN) in a very strategic manner. The use of BERT to the maximum possible extent of its contextual relationship and long-range dependencies in text, and at the same time, to include CNN layers and effectively identify important local feature patterns and salient phrases, is the strength of our model. This synergistic act makes it possible to perform better than the traditional models in capturing complex linguistic phenomenon that would otherwise be hard to capture. Our BERT-CNN hybrid model attains the state-of-theart performance of 94.7% on major benchmark datasets (e.g., IMDB, SST-2), by a significant margin over available baseline and advanced methods. The wide-ranging experimentation establishes that the combination of contextual embedding by BERT with localized feature extraction ability of CNNs offers an efficient and solid framework to advanced sentiment analysis in the various and difficult text information.

Diabetic retinopathy (DR) is an insidious ocular complication, with its initial stages being asymptomatic; therefore, accurate detection at an early stage is required to prevent severe complications. It describes a study which compares logistic regression to random forest models for diabetic retinopathy classification. The results indicate that for the purpose of diabetic retinopathy classification, the Logistic Regression is the strongest model from the point of view of recall and F1 score, both of which are critical to medical diagnostics, where false negatives should be minimized. Logistic Regression has better interpretability and thus is used for clinical decision-makers. Random forest is good to manage non-linear relation which indicates its strong model, however, lower F1 score gives us an information of it's not a good balance between precision and recall. Furthermore, future work can further improve classification performance by exploiting ensemble learning, performing feature engineering, and integrating deep learning. This work advances machine learning-based tests for diabetic retinopathy and highlights the need for high-recall models for use in medical screening.

With the ease of classifying land through satellite imaging, remote sensing has captured the Earth observation domain. Traditional methods for analyzing satellite images relied on manual feature extraction and statistical models which was time-consuming and often failed to capture sophisticated patterns. Moreover, advancements in artificial intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNN), have significantly improved classification accuracy. This study investigates the effectiveness of CNN-based architectures on the task of categorizing satellite images, by evaluating the performance of a custom CNN model against a pre-trained ResNet-18 model. The comparative analysis showed that ResNet-18 achieved better classification accuracy, computational efficiency and generalization ability than the custom CNN by achieving a 89.3% test accuracy with the reduced training time. These results indicate the power of transfer learning for the classification of satellite images and provide an efficient and practical means for the analysis of remote sensing data. According to the study, these deep learning classification algorithms isolate an effective and automated framework for handling large swathes of satellite imagery making it significant for environmental monitoring, land use planning, and disaster management.

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments.

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated.

This chapter explores the innovative applications of edible electronics for food quality monitoring, focusing on the development of edible tags and sensors embedded in food packaging materials. Edible electronics offer unique opportunities to enhance food safety and quality assurance by providing real-time monitoring of various parameters such as temperature, humidity, freshness, and contamination. The chapter discusses the design principles, fabrication techniques, and deployment strategies for edible electronic tags and sensors. It explores the integration of Internet of Things (IoT) technologies with edible electronics to enable seamless data transmission and analysis. Furthermore, the chapter examines the potential impact of artificial intelligence on enhancing the capabilities of edible electronics for food quality monitoring. Case studies and practical examples demonstrate the feasibility and effectiveness of using edible electronics in ensuring food safety and quality throughout the supply chain.

The spread of false information via fake news in recent years has raised serious issues and caused social discontent and division. It is critical to create efficient techniques for quickly and reliably recognizing bogus news. In the context of identifying fake news, this paper compares two popular machine learning techniques: LightGBM and Logistic Regression. Our analysis makes use of a dataset that includes both fake and real news pieces, with 23,481 fraudulent news stories and 21,417 real news stories. Important performance parameters including recall, accuracy, precision, F1-score, and Matthews correlation coefficient are taken into account while evaluating these algorithms. Our goal in doing this research is to clarify how effective these strategies are at solving the urgent problem of the spread of false information.

An effective solar forecasting is an important part of managing and enhancing the effectiveness of solar power systems. The purpose of this paper is to develop a distinct and smart energy prediction system for solar PV systems. An efficient Solar Synergistic Adversarial Energy Forecasting System (Solar-SAFS) has been developed, which greatly enhances the precision and reliability of solar power predictions. It combines various deep learning methods including Graph Convolutional Networks (GCNs), Variational Autoencoders (VAEs), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively understand spatial and time-related patterns in solar data. The strength and accuracy of this model are increased by using adversarial training, which makes sure it gives reliable predictions in different situations. For making the hyper parameters better and to increase model's effectiveness and efficiency, we are using Grey-Oystercatcher Hybrid Optimization (GOHO) technique. The Solar-SAFS is validated and tested with different data sets including Fingrid and DSK solar. It shows that this method gives better results compared to current deep learning models. Our findings suggest that Solar-SAFS improves the accuracy and stability of solar energy prediction by a large margin, offering an advanced solution for managing renewable energy resources. Moreover, it significantly helps to obtain better predictions for solar energy production, which is a crucial need in this sector. It also encourages more efficient and dependable forecasting methods for renewable energy sources like sunlight. Compared to existing deep learning models, the Solar-SAFS demonstrates significantly lower error rates across multiple performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Bias Error (MBE).

Ensuring data security and privacy is highly pertinent in today’s era marked by the dominance of collaborative machine learning. This study presents a novel framework, named" Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security," which leverages quantum computing to strengthen federated learning models against adversarial risks and enhance privacy safeguards. Our research investigates the incorporation of quantum key distribution and entanglement protocols into federated learning, therefore establishing a quantum- resilient methodology. The findings demonstrate the model’s ability to effectively resist advanced threats, hence assuring the maintenance of strong data security measures. The utilization of quantum technology in this context serves to strengthen the fundamental principles of cryptography, while simultaneously introducing a novel approach to enhancing privacy. Moreover, the study explores the potential of the framework to accommodate various datasets from different cooperating entities, while also resolving the difficulties presented by the diversity of the data. The framework’s success in accommodating multiple machine learning models and applications is demonstrated by evaluations of scalability and computing efficiency. In essence, the research paper titled" Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security" represents a significant progression in the field of safeguarding collaborative machine learning. The quantum-resilient paradigm not only increases privacy but also offers a safe foundation for the future of collaborative machine learning. The issue at hand pertains to the growing apprehensions over the safeguarding of data privacy and security within the context of our technologically advanced day.

Accurately predicting chronic conditions such as diabetes is essential for prompt treatment and improved patient outcomes. Using the UCI diabetes dataset, this work assesses four optimization algorithms: Adam, RMSprop, Nadam, and Stochastic Gradient Descent (SGD). The objective is to evaluate their efficacy in creating precise prediction models by empirical means. These algorithms have a significant impact on the stability and speed of convergence of deep learning models during training. With patient data, machine learning and deep learning models can be used to assess the risk of diabetes. Diabetes is a complex metabolic disease that requires early detection to prevent complications; however, the choice of optimizer has a significant impact on the models' efficacy. As the optimizer with the highest accuracy, precision, and F1 Score, Nadam stands out. SGD does exceptionally well in recall, while Adam and RMSprop do well as well. The choice of optimizer should take trade-offs between measures into account and be in line with specific therapeutic goals. They can be more effectively tuned as hyperparameters in diabetes prediction models. This work improves patient outcomes in the management of diabetes and healthcare risk assessment.

This chapter aims to delve into the practical applications and case studies of leveraging quantum computing for supply chain optimization. By exploring real-world examples and case studies, the chapter will illustrate how quantum computing technologies can revolutionize traditional supply chain management strategies. It will provide insights into how quantum algorithms can enhance optimization engines, improve logistics planning, and optimize resource allocation within supply chains. Through a combination of theoretical discussions and practical examples, this chapter will offer a comprehensive understanding of the transformative potential of quantum computing in supply chain optimization.

The use of the Inception-ResNet-V2 deep learning model and the LightGBM ensemble learning framework is proposed in this paper for the early detection of rice diseases. The system is trained on a labeled dataset of healthy and diseased rice plant images, enabling it to classify new images with high accuracy. This approach significantly enhances the ability to detect and respond to plant diseases, leading to improved crop yields and sustainable agriculture. The proposed smart application has the potential to greatly advance the accuracy and efficiency of disease detection in rice crops, benefiting both farmers and researchers. The findings from the experiments show that the suggested strategy is quite good at correctly identifying rice diseases, outperforming existing methods, and achieving high classification accuracy.

An exhaustive and comprehensive investigation was undertaken to address the critical issue of disease detection on apple leaves using cutting-edge deep learning techniques. The research delved into an array of diverse approaches, meticulously examining their efficacy and performance in disease detection, ultimately offering valuable insights into this vital domain. The research effort was marked by the exploration and application of a wide spectrum of deep learning models, each chosen for its distinct characteristics and potential advantages. The results of this extensive work were nothing short of remarkable. This study uses state-of-the-art deep learning techniques to present a thorough and rigorous analysis into the important problem of disease detection on apple leaves. Our research covers a wide range of approaches, all of which have been thoroughly assessed for their efficacy in the diagnosis of disease. We used a wide range of deep learning models, selected for their special qualities and possible benefits. The results of this extensive study are impressive and measurable. VGG-INCEP, the top approach, showed exceptional performance with a measured accuracy rate of 97%. The quantification of precision, recall, and F1 scores were 0.94, 0.92, and 0.92, respectively. Similarly, InceptionV3 yielded an F1 score of 0.93, precision of 0.95, and recall of 0.91, in addition to a measured accuracy of 97%. AlexNet consistently demonstrated measurable high precision (0.95) and recall (0.93), resulting in an F1 score of 0.93, despite a somewhat lower accuracy of 87%. The method's balanced performance is highlighted by these metrics. The study also evaluated the effectiveness of SVM, MobileNet, RCNN, and a recommended method. With quantifiable accuracy of 98% and quantifiable precision, recall, and F1 scores of 0.96, the suggested technique stood out. This assessment unequivocally shows that the suggested approach produces the best accuracy and overall performance and is distinguished by its measured precision and recall balance. It provides a numerical evidence of the method's efficacy in accurately detecting and categorising apple leaf diseases. The findings highlight the disparities in performance across the various models and highlight how the proposed approach, with its quantifiable excellence, has the potential to completely transform apple orchard disease detection.

The study promotes a thorough research effort to create a predictive model specifically for medical malpractice litigation, highlighting the intricate relationships between insurance and medical malpractice claims. The model that has been suggested incorporates sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to improve accuracy and forecasting skills. The study emphasizes the need for a comprehensive strategy that combines state-of-the-art prediction technology with a well-defined theoretical framework. The article provides a comprehensive overview of the topic by carefully examining the underlying factors that give rise to medical malpractice claims, which might range from poor recordkeeping to communication problems. It highlights the financial strain that healthcare providers and patients experience and suggest ways that insurance companies can better control risks, evaluate claims, and encourage preventive treatment. The performance of the suggested Random Forest and XGBoost models is presented in the results and comments, with XGBoost showing better predicting skills. The residual distribution and the accuracy of the models are clearly shown by the visualizations. To sum up, this study lays the groundwork for the development and application of predictive models and provides insurance companies with important information to help them negotiate the intricate terrain of medical malpractice in the healthcare industry.

Brain tumor detection has enhanced using a Multi-CNNs with Multimodal Information Fusion method has been proposed in this work. The proposed method leverages multiple modalities, like T1-weighted, T2-weighted, and FLAIR images, to create a fused representation of the input data. A deep neural network architecture using 3D Convolutional Neural Networks (3D-CNNs) is constructed to capture spatial and volumetric information present in medical images. The method also incorporates a real normalization layer and a weighted loss function to improve the learning process and prioritize accurate detection and localization of tumor lesions. Evaluating our method on a Kaggle dataset of 253 images (98 without tumor, 155 with tumor), we achieve superior results compared to existing approaches. Our proposed method attains an accuracy of 98.9%, sensitivity of 99.2%, specificity of 99.1%, recall of 98.7%, precision of 99.3%, and F1 score of 98.9%. Leveraging multimodal fusion, 3D-CNNs, and additional enhancements, our study contributes to more reliable brain tumor diagnosis, benefiting patients and healthcare providers.