Enhancing News Article Summarization with Machine Learning

The exponential growth of online news content has created a pressing need for automated summarization tools to help process and condense information effectively. This paper presents a machine learning-based approach to summarizing news articles, focusing on techniques that produce concise and coherent summaries. The methodology includes text preprocessing steps such as tokenization, stop-word removal, and stemming, followed by feature extraction and model training using machine learning frameworks. Libraries such as NLTK and TensorFlow are employed to facilitate text processing and the implementation of the summarization model. The proposed approach is evaluated against baseline models, showcasing its ability to generate high-quality summaries efficiently. The research highlights the advantages of machine learning in automating news summarization, saving time and effort for readers and editors. Challenges such as handling nuanced language and context are discussed, and the paper outlines future research directions to address these limitations and further enhance summarization performance. This study contributes to the growing field of automated news summarization by providing a practical, scalable, and effective solution. It underscores the potential of machine learning to revolutionize how news content is consumed and processed, offering valuable insights for advancing this domain

A Systematic Literature Review of During and Post-treatment Sleep Disturbances in Breast Cancer Patients

Breast cancer patients in India suffered with various post treatment symptoms even after many years of treatment completion. Sleep problems are most dominant issue among all the issues among fatigue, stress, mood swings etc. There is growing need to assess the sleep quality of the patients prior, during and after the treatment. Although oncologists have been working widely for the better treatment facilities resulting the higher survival rates, it is critical to keep track of good quality of sleep and maintaining it during the treatment. Out of 2180 Scopus indexed research papers 32 were selected after rigorous selection criteria. Supportive Care in Cancer published higher number of publications whereas Journal of Pain and Symptom Management received the highest number of citations. This article explores the evidences of sleep problems in breast cancer patients, an overview of significant studies as evidences of treatment induced symptoms, related factors, solution for burdensome symptoms, and how machine learning techniques can be incorporated into sleep quality prediction in breast cancer patients

Leveraging AI for Accurate Time Series Forecasting

This study seeks to develop a robust model for forecasting time series data, with an eye towards complex temporal datasets. Accurate forecasting in time series analysis is a function of past information and constitutes a basis for unsupervised machine learning. With deep learning techniques such as neural networks, this work seeks to provide high accuracy over traditional approaches in time series forecasting. Such complex techniques have a significant impact in overcoming complications in forecasting in areas such as weather trends, consumption of energy, and financial trends in the marketplace. Out of such techniques, Artificial Neural Networks have been seen to outshine alternatives such as Long Short-Term Memory networks in working with complex temporal relationships. In this work, an opportunity for leveraging complex AI techniques towards enhancing accuracy and dependability in forecasting in a time series is focused on

OptiMediaAI :Transforming Customer Support with AI-Driven Video Innovation

In a customer-first era, effective care is paramount in driving satisfaction and loyalty. OptiMediaAI, an AI-powered video care platform, revolutionizes customer experiences with state-of-the-art technology including AI, machine learning, video communications, and emotion analysis. Personalized, empathetic, and effective contact through NLP, emotion analysis, and gesture analysis enables deeper relationships and reduced attrition of customers. The solution integrates face recognition, speech-to-text, and LSTM-powered chatbots for inclusivity, correct communications, and real-time responsiveness. Meeting both apparent and unobvious customer needs, OptiMediaAI maximizes fulfillment and enables operational perfection. As a 24x7 AI service agent, it transforms customer care into a real-time and efficient experience, driving business and supporting economic growth. OptiMediaAI is an AI-powered customer care breakthrough innovation

Ethical Algorithms for Machine Learning Based Ai Powered Robotics: Ethical Perspective for Covid-19 Like Health Emergencies

The coronavirus, a dangerous member of the virus family, infected millions globally. Many peoples were gone infected with it. The of infected peoples were increasing very fast day by day. Now a day’s total cases were about 110M, recoveries about 60M and deaths about 3M. For this, we use AI means artificial intelligence in health care. We can ethical trained robots through machine learning in the situation of covid pandemic. We made robots that help staff in their personal care touch. They go to patient and met the live to doctors by the help of a tab that can we insert in them. They make patients happy by play songs, talk and motivate them. We can insert camera on their head with that they scan the patients that they are happy, sad or depressed. They also help doctors to make the vaccines of this deadliest virus. Robots can be very useful for doctors. The pandemic has disrupted machine learning, analytics, and data for large companies around the world. Now is a good time to look at what that means for leaders who depends on these tools, and what these leaders are doing to regroup and recruit

Enhanced Machine Learning Model for CVD Prediction Using Principal Component Analysis (PCA)

The World Health Organization (WHO) report says that each year, cardiovascular diseases are  the leading reason for around 17.9 million deaths across the globe. This is a more serious problem in low- and middle-income countries where there are barriers to early check-ups and specific treatments. The quicker and better detection of heart attacks helps reduce the risk of death. Based on previous methods, the study takes the Cleveland Heart Disease dataset from the UCI Machine Learning repository and uses it to design and check best machine learning models that take advantage of standardization, Principal Component Analysis (PCA) and  hyperparameter tuning. We used machine learning algorithms such as Support Vector Machine, k-Nearest Neighbors, Logistic Regression and a Multi Layer Perceptron model, all combined under a Voting Classifier. With a 98.33% accuracy, 98.25% F1-score, 96.55% precision, and 100% recall on test data, the enhanced hybrid model (Voting Classifier) leaves all other models far behind in performance. The hybrid model had small gaps between train and test values for metrics, with 1.24% accuracy difference, 1.29% F1-score difference, 3.45% precision difference and -0.92% recall difference. Incorporating Principal Component Analysis (PCA) lowered the number of dimensions used while increasing accuracy, precision and F1 scores for a number of models. The results suggest that the use of Principal Component Analysis (PCA)-combined hybrid models leads to better, more understandable and trustworthy tools for predicting CVD. Strengthening predictive models for CVD risk assessment is now possible, supporting prompt clinical choices and helping patients improve

Role of AI In Water Pollution

A major cause of threat to human life and ecosystems is water pollution. The development of innovative solutions with the latest technologies can solve the problem of water pollution around the world. This research paper guides how water pollution management can be done by AI using the combination of AI algorithms with machine learning and data analytics. This shows how we can detect, monitor, and remediate water pollution in real time through intelligent systems. We look at how large amounts of water body data are collected by the sensors powered by AI. The identification of pollutant patterns and prediction of pollution events can be analyzed by advanced machine learning models and deep learning models. This paper guides people on how AI can help in understanding and being more involved in water management. The use of interactive visualization tools makes it easier for people to understand and act on pollution data. AI with the help of different sensors and monitoring systems be a guide in the reduction of water pollution. With the help of AI, we can also make sure that water is safe and clean for future generations. AI plays a huge role in the conservation of water from different water pollutants. Not only because it can help make pollution control more accurate, but it can also help create a more sustainable future

Sustainable Truck Overload Management Framework

The Sensor for Overloading of Trucks project seeks to develop an advanced sensor mechanism with a high accuracy for checking whether a truck is overloaded or not. Eliminating overloading in trucks is critical for effective loading and weighing, reducing mechanical failure, minimizing deterioration in roads, and enhancing overall security policies in terms of roads. Overloading is one of the most important factors in causing accidents, infrastructure deterioration, and increased maintenance, and its management is a matter of high concern. The system developed in this work utilizes strain sensors for monitoring the compressive and tensional loads experienced at specific parts of a truck at which most strain is encountered. Measuring such a process, nevertheless, proves to be a challenge with a moving truck, whose motion generates variable and unpredictable jerks and rough roads, and temporarily generates fluctuations in strain, creating a problem in taking proper readings. The work seeks to overcome such complications through a robust and effective model of a sensor capable of working under such variable motion and providing proper readings for weighing and supporting safer transportation processes

Contrasting Synthetic and Real Art: Pioneering AI Learning Advancements

This paper presents a comparative study between models trained on real-world and synthetic datasets in the domain of artificial intelligence and machine learning. By meticulously evaluating model performance, generalization capabilities, and robustness across diverse scenarios, the investigation of the efficacy and feasibility of synthetic data in machine learning applications. Through empirical analysis, the address fundamental questions regarding predictive accuracy, resilience to adversarial inputs, and biases inherent in synthetic data. Our findings provide valuable insights for practitioners and researchers navigating the dynamic landscape of AI methodologies, offering guidance for informed decision-making and future advancements in the field

The Influence of Global Trade Policies on Business Development in Emerging Markets

This study explores the complex relationship between global trade policies and business development in emerging markets. As globalization transforms economic environments, comprehending the ramifications of trade policy is becoming increasingly vital for enterprises in developing countries. This study examines the impact of trade agreements, tariffs, and regulatory frameworks on market accessibility, competitiveness, and growth prospects for enterprises in emerging markets. The research analyses case studies from many areas, emphasizing the dual role of trade policy as both a driver of growth and a barrier for local businesses. This study utilizes a qualitative technique, using case studies and interviews with industry experts, legislators, and corporate executives. The findings highlight the imperative for politicians and business leaders to cooperate in establishing favourable conditions that promote sustainable company growth in emerging nations