The Global Research Network for Verified Scholars

Autism Spectrum Disorder (ASD) is an intricate neurodevelopment disorder in which the expression of communication patterns and language patterns is deviated in comparison to normal development. Conventional diagnostic methods are quite intensive on judgmental clinical Newman in many cases, thus results may be slow and unreliable. The research presents a diagnostic model in the form of a transformer that is aimed at improving the accuracy and speed of ASD detection with the help of advanced Natural Language Processing (NLP) methods. The suggested system takes advantage of rich contextual and semantic representations of both transcript speech and text-based speech of ASD patients and controls based on a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) representation. The model can easily learn fine linguistic and pragmatic anomalies relevant to the language and communication in autistic speech by being trained on domain-specific corpora. Ignoring hyper parameters and performing experimental tests on benchmark datasets show that the fine-tuned BERT model drastically surpasses instead of heterogeneous machine learning models including Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks in terms of classification accuracy and F1-scores. Moreover, applications of interpretability with SHAP values and attention-based visualization identify essential linguistic characteristics that lead to ASD diagnosis to guarantee transparency and clinical usefulness. The study outlines the possibilities of transformer based linguistic modeling as a scalable, interpretable, and objective method of screening ASD at early levels.

Techno-Economic Assessment and Optimization of Renewable Penetration in Power Grids: Challenges and Solutions"*

The economy of our country depends on crop cultivation. In India, agriculture serves as the backbone of a growing financial system, so maintaining this financial development is crucial. It makes a significant contribution to the global economic and agricultural prosperity of all nations. Unpredictable weather conditions and soil parameters are frequently the cause of low crop productivity. The main objective is to suggest an ML (machine learning) based agriculture system that can assist farmers regarding crops that can be harvested with specific values of soil and environmental parameters. Through several benchmark tests, LightGBM demonstrates improved performance in terms of prediction accuracy, model stability, and computing efficiency. This paper also evaluates the elements necessary to guarantee the optimal functioning of the crop recommendation system.

The use of effective IOT devices and decision learning for the prediction of crop growth in the agriculture field is encouraging ways to boost economic growth in the farming sector. Increasing operating costs and degradation of the atmosphere are the key issues in the area of agriculture. A predictive model with advanced data analysis is needed to process massive amounts of data collected through multivariate sensors deployed in the agriculture field. In classification predictive modeling achieving high accuracy is extremely challenging due to the high-imbalance characteristics of training data. There is a need to improve the classification performance of imbalanced data, which happens when there are insufficient instances of the data that represent either of the class labels. That also affects the robustness of the predictive model and significantly causes the loss of essential crop growth information and crucial details

This paper proposes an Optimized Cloud Intrusion Detection Framework (OCIDF) that uses multi-classifier security algorithms to improve detection performance of cloud systems in terms accuracy, adaptability, and efficiency. OCIDF uniquely combines decision-tree-based learners, ensemble boosted models, and lightweight deep neural networks into an adaptive and optimized layer, where the weights of classifiers are balanced dynamically against accuracy, latency and resource consumption. The framework is also designed to dynamically select features and have a feedback learning loop to adapt to new attack pattern, to guarantee strong resistant to zero-day threats. Experimental analysis indicates that OCIDF drastically outsmarts the current solutions, attaining 96%, 94%, 93%, and 93.5% accuracies, precision, recall, and F1-scores, respectively, and has a high throughput of 3200 instances per second