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A growing field in India that can assist farmers in many ways is the identification of plant diseases. Plant disease reduces the mass production in agriculture. Every time due to heavy loss in agriculture, farmers face many challenges to raise up their needs. This study analyzes various diseases that affect various plants due to the following criteria like environmental condition, types of plant formed from Monocot and Dicot family, crop rotation and Ph value of the soil. The existing system analyzed the diseases based on one of the criteria. Although the strategy is to be followed to identify plant diseases at the early stage with various aspects so that the production in agriculture could be increased. The majority of machine learning algorithms already exist and may be improved upon to yield more accuracy. Xanthomonas wilt, Black and Yellow Sigatoka, Bunchy Top Virus, Black Sigatoka, Fusarium wilt, Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD), Yellow vein mosaic virus, and Powdery Mildew are a few of the diseases of the plants grown on crop rotation which are the subject of study of this paper. Plant illnesses are classified according to disease in the many open-source datasets that are accessible. For deeper understanding, we have provided a list of references. Here, we've examined the approaches that are now in use for automatically detecting various diseases of plant leaves. We present an approach for accurately identifying early signs of plant diseases from a variety of perspectives. [ABSTRACT FROM AUTHOR]

Floods in Malaysia are described as flash floods and monsoon floods by the Department of Irrigation and Drainage (DID). Forecasting is a specialized form of predictive analysis used to predict future trends or behavior from existing data. Weather forecasting predicts the weather in the future from current data circumstances. As flood forecasting models become more accurate, however, their capacity to accurately predict flooding decreased as the forecast continues. Therefore, to understand the different techniques used to forecast flood levels, a systematic review of the literature was conducted. This paper's main objective is to examine the most common variable used to forecast floods, utilizing the systematic review technique. From the main focus, we can identify the research questions, such as the most commonly used prediction method and its accuracy. In the end, two of the most common variables used for flood forecasting are rainfall and water level. This study's data can help others forecast floods using standard variables that yield the best accuracy possible. [ABSTRACT FROM AUTHOR]

Recommender systems are among the utmost popular and successful uses of machine learning in the corporate world. Recently, these schemes have made use of ML methods from the artificial intelligence domain. Recommender systems are used to provide guidance to individuals in all fields of technology, and they are crucial for each domain. There are many different types of recommendation systems, such as content-based and hybrid-based, collaborative filtering-based. The collaborative filtering method is used in this paper for recommending the movies. In this field, the effectiveness of algorithms based on CF (collaborative filtering), which take advantage of user and item behaviour, has been established. In this paper, we have analysed five different similarity metrics on the dataset. The similarity metrics are cosine similarity, Euclidean, Pearson, Jaccard, and MSD with the KNN algorithm to predict outcomes. Results are evaluated by using different metrics which are RMSE, and MAE, and at the end there is a comparison between the item-based collaborative filtering and the user-based one approach to know which approach remains better. [ABSTRACT FROM AUTHOR]

Autonomous vehicle is a main direction of the current automobile development. The implementation of autonomous driving depends on the perception, decision-making, and control functions of the vehicle. Path planning is an important component of decision-making. Nowadays the path planning algorithm of autonomous driving system is greatly affected by the environment and cannot be applied to complex road environment. Based on this, this paper summarizes some path planning techniques, introduces the definition and importance of path planning, and expounds the commonly used path planning algorithms. This article provides an in-depth analysis of traditional methods, such as Dijkstra algorithm and A* algorithm, and discusses their advantages and limitations in handling real-world scenarios. The article also discusses some advanced technologies, such as Rapid-exploration Random Tree(RRT) algorithm and ant colony algorithm. These methods utilize advanced algorithms and machine learning to guide vehicles through complex and dynamic environments. In addition, this article also summarizes some improvement methods based on the above algorithms, such as combining Dijkstra algorithm with RRT algorithm to form Dijkstra-RRT algorithm. This paper can provide valuable insights into various path planning techniques for autonomous vehicles and assist in the further development of autonomous driving technology. [ABSTRACT FROM AUTHOR]

This academic paper explores the complex utilization of integrated task and motion planning (ITMP) in multi-robot systems. Given the rapid advancement of robotics and automation, multi-robot systems have gained increasing attention in various applications, such as smart manufacturing, logistics storage, environmental surveillance, and dual or multi-armed robotic platforms. The fusion of task and motion planning has become a key technology in the development of multi-robot systems. Initially, the paper suggests numerous strategies to integrate task and motion data to improve planning efficiency, thus boosting the motion planning efficiency of robots during task execution. It then investigates the use of learning techniques, such as reinforcement learning and deep learning, to enhance task and motion planning. Ultimately, it presents experimental evidence to support the claim that the integration of task and motion planning can effectively increase the efficiency of multi-robot systems and optimize their performance. The paper also examines modeling methods for multi-robot systems, typically incorporating task data with motion data to better guide robots during task execution. It presents shared space graph methods, sample-based planning methods, and market-based methods. Furthermore, it reviews learning models used to train and optimize machine learning models in ITMP. It introduces the INFORMED algorithm, a learning search issue in the field of task and motion planning that employs multilayer perceptron (MLP) and graph neural networks (GNN) to model relationships between streams and objects. Through this paper, the authors aim to offer valuable insights for the research and application of ITMP in multi-robot systems, thereby promoting further progress in this area. [ABSTRACT FROM AUTHOR]

In the modern age, a significant number of official documents, including IDs approved by the government and certificates, are predominantly image-based or paper-based. Extracting, entering, and searching for information from these documents manually can be a cumbersome and time-consuming process for organizations. To address this challenge, Automated ID Document Extraction and Classification (AIDEX) is introduced. This model utilizes machine learning algorithms to automatically classify identity documents issued by the government into predetermined categories such as AADHAAR card, Driving License and PAN card. By identifying unique indicators present in these ID proofs, AIDEX effectively detects and displays vital information, eliminating the need for manual intervention and achieving automation. Through appropriate access and permissions, the extracted data can be securely saved and accessed by authenticated users as required. The application of this strategy significantly reduces the time spent on physical labor, leading to resource conservation. Notably, AIDEX stands out by integrating the identification and data extraction from images of Driving Licenses, distinguishing itself as a novel approach. This paper provides a comprehensive overview of the objectives set for AIDEX, the various techniques employed during its development, and the technical specifications of the resulting model. The findings highlight the potential of AIDEX to streamline information retrieval processes, enhance operational efficiency, and promote automation in handling government-issued identity documents. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has been widely used across the oil and gas industries that elevate asset management efficiency. Nevertheless, as the industry produces hydrocarbons, reducing CO2 emissions becomes one of the challenges. Therefore, this paper illustrates the utilization of AI to enhance the gas lift operation using pattern recognition and optimization algorithms. Initially, the feature extractor was developed using self-supervised learning to learn features from data without manual labels. Then, the patterns are clustered to build a pattern recognition model using a supervised classification model. Afterward, an optimization algorithm is applied to optimize fluid production under allowable operational and CO2 emission limits. Then, a gas lift stable production model was developed using Machine Learning (ML), relying on controllable and reservoir parameters that will be used for optimization purposes. The study used synthetic data from eleven wells with similar wellbore and mandrel configurations with various depth and Productivity Index (PI) and constant reservoir pressure. An air compressor specification and specific CO2 emission were also determined. In addition, gas injection and adjustable choke are assumed as controllable parameters in the optimization process. This study shows a high prediction model accuracy of 0.99 for K-Nearest Neighbor classifier (KNN) and an R2 score of 0.99 for Multi Layers Perceptron (MLP) as a regressor. As regards the optimization algorithm, Simulated Annealing (SA) was used to achieve the optimum solution in a reasonable time, especially for a near real-time environment. Lastly, the workflow introduced in this paper could be implemented in a real case as the study used widely known information in gas lift operations. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) transforming the Insurance industry by improving efficiency, reducing costs, and providing a better customer experience. As these technologies continue to evolve, more innovation can be expected in the Insurance industry. A Bibliographic analysis is conducted for scientific mapping based on 1,084 SCOPUS-indexed publications between the year 2000-2022 using VOSviewer Application. The Analysis was conducted based on Publications by year, Source, Author, Affiliation, Country, Type, Subject Area and Funding Sponsors. The research found the result, the year 2022 had the highest publication of 203, through documents per year by the source of Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics at 43 publications, Bauder, R.A. author is the maximum contributor, Harvard Medical School has been the major affiliate, United States of America, has the maximum 341 publications, Conference paper has the majority participation at 412 documents at 38 percent and Keywords "Artificial intelligence", Machine Learning" and "Insurance" has the highest occurrence. The maximum number of publications inthe field of computer science at 28 percent. Overall, this bibliographic analysis provides a comprehensive overview of the current state of research in AI and Machine Learning in the Insurance industry and highlights the potential for further innovation and development in this field. [ABSTRACT FROM AUTHOR]

In the present day, deep learning applications are widely used in every field of development. In this paper, such a type of application is discussed in real-time. Mura dataset is the largest abnormal dataset given by the University of Stanford. In this research paper, we will understand the pre-processing of the image dataset. Kera's and TensorFlow libraries are used to build this model. We are classifying images using different ML models where accuracy is compared to get the best model build. This paper gives us a clear understanding of building deep learning models, understanding pre-processing techniques, and working on heavy data. This research's main agenda is to create a model which can classify bone fracture images and also give a small path for further development in the deep learning area. In this project we are using Resnet50, InceptionV3, Mobile Net and DenseNet with optimizer Adam and comparing their accuracies graphically. [ABSTRACT FROM AUTHOR]

With the advancement of technology, computer network usage has significantly increased. Although everyone in today's world rely on the use of internet for the fulfillment of the day-to-day work Security issues are one of the concerns. Intrusion Detection System based on ML models are more structured and meticulous. In this paper we have discussed various datasets used in intrusion detection systems. i.e. (DARPA, KDD CUP 99, NSL-KDD, UNR-IDD, KYOTO, ADFA(LD), ADFA(WD) (Australian Defense Force Academy), CICIDS, LU-FLOW, UNSW-NB15, NF-UQ-NIDS-v2, etc.). Comparing UNSW-NB15 Machine learning models were used to Analyses the Dataset. The aim of this paper is to analysis all the datasets together to get the preformation of it. [ABSTRACT FROM AUTHOR]

Cybercrime has been on a massive increase for several years. As more individuals conduct business and live their lives online, more criminals are turning to the internet to steal. With the developments in cybersecurity, adoption of best practices, cybersecurity awareness initiatives, and increased regulations and partnerships between businesses and governments to resolve the problems, cybercriminals may be feeling the pressure. In this paper, we are going to predict operating system vulnerabilities using machine learning algorithms. And evaluate their accuracies through training them. The aim of the paper is to use Machine Learning algorithms to predict with accuracy operating systems vulnerabilities. Different ML algorithms have been analyzed, such as Logistic Regression, Support Vector Machine and Random Forest. And, out of all of these algorithms, Random Forest had the highest accuracy of 99.33%. [ABSTRACT FROM AUTHOR]

The stock market has witnessed a remarkable surge in popularity in recent years, attracting investors from all walks of life. However, predicting stock values remains a daunting task due to financial markets' inherent unpredictability and complexity. Despite these challenges, the stock market offers a dynamic and ever-changing platform for traders to invest in shares, with the potential for significant gains and losses. For investors, accurate forecasting of stock prices is crucial as it provides invaluable insights into a company's financial health and growth prospects. With this information, investors can make informed decisions, mitigate risks, and capitalize on lucrative opportunities in the market. As a result, extensive research has been dedicated to developing effective prediction methods, leveraging various mathematical models and machine-learning techniques. This research paper delves into the realm of stock market prediction, explicitly focusing on evaluating different machine-learning styles. The primary objective is to comprehensively analyze and compare the performance of these techniques in forecasting stock market behavior. By understanding the strengths and limitations of each method, investors, financial analysts, and market participants can gain critical knowledge to optimize their trading strategies and decision-making processes. To achieve this goal, the study explores an array of machine-learning algorithms, ranging from traditional linear regression models to sophisticated deep-learning approaches. These algorithms leverage historical stock market data, macroeconomic indicators, company financials, and sentiment analysis, among other factors, to predict future price movements and market trends. In addition to performance comparison, the research paper examines the impact of various factors that influence the effectiveness of these machine-learning techniques. Factors such as data quality, feature engineering, model selection, hyperparameter tuning, and market conditions play pivotal roles in the accuracy of predictions. Understanding these factors will aid in refining the model-building process and enhancing overall forecasting capabilities. The study encompasses an extensive dataset spanning multiple stock markets and periods, ensuring robustness and reliability in the findings. Performance evaluation metrics, including mean squared error, accuracy, precision, recall, and F1 score, will be employed to assess the predictive power of the machine-learning techniques objectively. Furthermore, the paper investigates the potential of ensemble methods, combining the strengths of multiple models to achieve enhanced prediction accuracy. Ensemble techniques, such as bagging, boosting, and stacking, have proven effective in diverse domains and are expected to demonstrate their value in stock market prediction. By the end of this research, readers will have a comprehensive understanding of the landscape of machine-learning techniques applied to stock market prediction. The findings will offer insights into which methods are most suitable for different market conditions and will aid in establishing best practices for effective and reliable stock market forecasting. In conclusion, this research paper serves as a valuable resource for investors, financial analysts, and researchers, thoroughly assessing machine-learning techniques' efficacy in predicting stock market behavior. It contributes to the growing body of knowledge in financial technology. It underscores the critical role of data-driven decision-making in navigating the complexities of the modern stock market. [ABSTRACT FROM AUTHOR]

Clinical laboratory testing is a crucial part of delivering high-quality health care. Lab tests are ordered by a doctor or another clinician to determine, control, or demonstrate a patient's condition. The process starts with the preparation of a pattern of the patient's blood, tissue, or other organic material, which is then sent to a lab to be uniquely identified and checked to make sure it is appropriate for the test. Some exams are graded by hand, but the majority are completed with the aid of technically advanced equipment. The electrocardiogram (ECG)is a crucial tool for identifying cardiovascular issues. The huge number of ECG records were formerly recorded on paper. Examining paper ECG recordings manually can be challenging and time-consuming. The Machine learning algorithms, Support vector machine and other algorithms in particular show promise for systems that have abilities to detect cardiovascular illnesses where the effectiveness differs in terms of accuracy, recall and precision. The proposed research provides a thorough analysis of several data models for predicting patient illness for clinical diagnosis in cardiology. [ABSTRACT FROM AUTHOR]

Various machine learning algorithms are developed with an aim to create precise and trustworthy models and extract knowledge from data sources. Deep expertise in the field of machine learning is required for the challenging task of choosing the right algorithms for a specific dataset. There is no single algorithm that outperforms all others across all applications and different datasets. The difficulty of choosing an appropriate algorithm for a specific task in specific domain is related to the properties of the dataset. Properties of the dataset are measured through meta-features. Meta-features describe task and can provide explanation how one machine learning approach outperforms other algorithms on a given dataset. Learning about the effectiveness of learning algorithms, or meta-learning was developed to deal with this issue. Focus is required because previous research papers have not successfully identified meta-features in particular domains. In this research, we have evaluated various meta-feature characterization methodologies and have concentrated on basic meta-features. Business domain data is in the focus of this paper. We computed basic (general) meta-features and illustrated several use cases for their applications. [ABSTRACT FROM AUTHOR]

There are various models out there, both theoretical and experimental, which are used to predict the fatigue crack growth in metals. Machine learning based computational models have been recently developed to predict such crack growth rate in a more comprehensive manner. This paper presents a supervised machine learning model with a better accuracy than the existing machine learning models. The experimental data obtained from the fatigue test carried out on CT specimen of material SA516Gr70 is used to test and train the model. The machine learning model XGBoost, which is implemented in this paper, is not only more accurate but also less likely to over fit when exposed to unseen data. Three alternative algorithms are investigated and by the process of elimination, best one is chosen. The paper shows the results received from some of those models, which were important in order to select the best one. The metrics used to filter and tune the model's performance are R2 score and mean squared error. The procedure for finding the optimal model and its results have been discussed. [ABSTRACT FROM AUTHOR]

Face Emotion Detection Music Player is an innovative idea that combines computer vision and machine learning techniques to create a unique and interactive music player. This Research Paper studies the uses of Convolutional Neural Networks (CNN) to detect and analyze facial emotions in real-time and dynamically create playlists based on detected emotions. The music player is designed to record facial expressions using a webcam or other suitable camera. To reliably identify facial expressions including happiness, sorrow, rage, surprise, and more, a CNN model is trained on a sizable collection of face images. The trained model demonstrated an astounding 74.46% accuracy in identifying emotions when combined with music player software running on a PC or other suitable device. The CNN model analyses the photos to determine the user's emotional state as they interact with the music player in real-time, as captured by the camera. Based on the detected emotions, the player automatically selects and plays songs from a predefined music collection that matches the user's emotional state. For example, if the user looks happy, the player can play happy and energetic songs, while if the user looks angry, it can play more calming and soothing music. Face Detection Music Player offers a personalized and dynamic music listening experience as the music selection is constantly updated according to the user's emotions. This research paper illustrates how computer vision and machine learning techniques may be used to produce interactive programs that can instantly adjust to the moods and preferences of their users. It has uses in user experience research, entertainment, and mental health, among others. [ABSTRACT FROM AUTHOR]

This state-of-the-art review paper provides a comprehensive overview of meta-learning techniques for learning to learn in machine learning. Meta-learning called "learning to learn," is an approach that enables models to learn from new tasks learning quickly and efficiently, based on prior knowledge and experience. The paper discusses various meta-learning techniques, such as model-based meta-learning, metric-based meta-learning, optimization-based meta-learning, and memory-based meta-learning. The review also covers the applications of meta-learning in different domains, including natural language processing, computer vision, and reinforcement learning. Additionally, the paper discusses the benchmark datasets, challenges along with the future directions of meta-learning research, highlighting the need for developing more efficient and scalable meta-learning algorithms. Moreover, the proposed Meta-Learn framework incorporates a hierarchical structure to facilitate knowledge transfer across multiple levels of abstraction, and it also includes a memory component that enables the system to store and reuse previous experiences. Overall, this review aims to provide a comprehensive overview of Meta-Learning techniques for learning to learn in Machine Learning and proposes a novel Meta-Learn framework that could potentially enhance the performance and scalability of Meta-Learning algorithms. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak impacted drastically to education and most of educational institutions started preferring online education for students. However, after the settlement of the pandemic there is uncertainty among people about whether they should prefer online education for furthermore or start in offline mode to make it more interactive, so this paper is about an analysis of people's sentiments and emotions through Tweets about COVID-19 Education. This paper aims to study the reaction of people around the world toward online education during COVID-19. This study is conducted on the basis of the responses of students, teachers, parents, college professors, etc. We started with labeling the data into three sentiments namely positive, neutral, and negative and for validation then we used Machine learning (ML) classifiers namely, Logistic regression, Decision tree, Random Forest, Multilayer Perceptron (MLP), Naïve Bayes, Support vector machine (SVM), K-nearest neighbors (KNN), and XG-Boost. Then we performed emotion detection by considering 5 emotions namely happy, surprise, sad, fear, and angry and for validation we used ML classifiers. After applying all these ML approaches, the XG Boost ML classifier achieved the highest accuracy of 94% in classifying the tweets as positive, neutral, or negative, and 96% accuracy in classifying the tweets as happy, surprised, sad, fearful, or angry. [ABSTRACT FROM AUTHOR]

One of the main forces advancing Industry 4.0, also known as the fourth industrial revolution, is machine learning (ML). This paper examines the machine learning (ML) models application for biogas production prediction in anaerobic digestion (AD). This study's primary objective is to determine which ML techniques and models are utilised in the AD process. In addition, this study identifies the type of ML techniques, input and output parameters, and software used. Researchers have widely employed a couple of ML models in biogas production. After reviewing the 15 most recent papers, it was discovered that the Artificial Neural Network (ANN) and Adaptive Network-Based Fuzzy Inference System (ANFIS) are the most commonly used types of ML. The most commonly used operating parameters in predicting biogas production were reaction time, temperature, pH, total solids (TS), volatile solids, volatile fatty acids (VFAs), and fixed solids. In conclusion, the review discusses the challenges and prospects of using machine learning in the AD process and provides recommendations for future implementation. [ABSTRACT FROM AUTHOR]

The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented modifications such as integrating the reverse learning algorithm and adding elite opposition solution to create the modified Sine and Cosine Algorithm (the modified SCA). Furthermore, by combining the fuzzy k-nearest neighbor method with the modified SCA, the study simulates numeric datasets with two or multiple classes, and analyzes the results. The accuracy rate (ACC) achieved by the modified SCA FKNN in this paper is compared to other models, with data comparison results and tables presented for each. The modified SCA FKNN proposed in this paper has obvious advantages on accuracy rate(ACC). [ABSTRACT FROM AUTHOR]

In the machine learning era, aspect-based sentiment analysis is a very popular idea that is still in the research realm. The main objective of this paper is to derive new pattern-based rules for Aspect Extraction. This focuses on the Explicit aspects where the aspect is precisely defined, unlike the previously researched papers on Aspect Extraction, this model does not limit itself to the noun/noun phrases but also focuses on the other words like verbs in a review. Newly extracted pattern rules for aspect extraction would be proved beneficial in terms of sentiment analysis of online customers. [ABSTRACT FROM AUTHOR]

Today, the world is exposed to many widespread health crises. One of these crises is the emergence and spread of the covid-19 virus, where any person becomes infected with this disease through the rapid transmission of infection between people, either by palpation or by inhaling droplets of carriers of this virus. Hence, there is a need to build a tracking system to respond quickly to problems. This paper proposes using one of the unsupervised machine learning algorithms, the K-Means Clustering Data, to determine the areas with the most significant percentage of infections by dividing them into infected and uninfected human groups. Where the infected persons are divided into three high groups (C1), medium (C2), and low (C3), and follow the path of the infected persons using Google Maps. The contribution to this paper is to support appropriate decision-making by public health authorities by predicting endemic areas and how to address problems that occur as they can use this system to reduce other infectious diseases. [ABSTRACT FROM AUTHOR]

Twitter is known to be one of the familiar social networking platform these days, among many others, with a lot of user engagement. This microblogging site encourages social interactions, allowing users to stay up to date on the latest news and events and share them with others in real time. Tweets are limited to 280 characters and is allowed to include links to related websites and tools. With a platform having such wide reach, it is prone to be targeted negatively and spams are one way to do it. Spammers use this platform to display malicious content that is inappropriate and harmful to users worldwide. Machine Learning uses various approaches that can be used to detect spam and overcome it. However, with the advent of recent technologies it has been observed that the properties of tweets vary overtime making it difficult to detect spam leading to the "Twitter Spam Drift" problem. This paper reviews the papers published since 2018 that have focused on the spam drift problem and gives a comparative analysis of the different algorithms that are utilized on the various data sets to tackle such a problem. [ABSTRACT FROM AUTHOR]

Health and medicine has gained a lot of importance in today's digital world, where evolving technology is being used to fight against almost all the known diseases thoroughly. But, according to reports, more than two lakh people in China and one lakh in United States of America are dead every year due to the mistakes made while prescribing errors. This paper aims to propose a system which takes as input the symptoms from the patient to predict the disease, which is followed by recommending the correct medicine. This system consists of database system module where disease dataset contains attributes like "DiseaseID", "Disease", "Symptoms" and "count of occurrences" and drug dataset has additional attributes such as "drug" and "drug_rating". In the data preparation module, a new data frame is created where each disease with its associated symptom is given "1" and "0" if it is not the symptom related. After all these steps of preprocessing are performed on the raw dataset, the dataset is now ready to be acted upon by a machine learning algorithm. Models like Decision Tree, Random Forest and Naive Bayes were applied on the training-dataset. Entire knowledge database was fitted in models and it was seen if not much dataset is used Random Forest And Naïve Bayes classifiers showed 84% accuracy whereas Decision Tree gives 92%. But Random Forest also gives accuracy similar to Decision Tree for large dataset. Hence for disease prediction we use Decision Tree. The recommendation is done on the basis of the rating of the drug. After this, only the drug with the highest rating is included in a newly formed dataset and the rest of the drugs are ignored. The system is implemented for user access by making use of Tkinter a python GUI Library for constructing the desktop end user interface. This paper deals with the design and implementation of a system which performs the function of prediction of diseases and the recommendation of medicines, which adds to the capabilities of the present systems in health infrastructure. [ABSTRACT FROM AUTHOR]

This paper discusses a keen way to manage and distribute water from a water tank or reservoir to consumers with minimal wastage. The aim of this paper is to discuss about the implementation of an IoT (Internet of Things) based water distribution system that will diminish the water wastage by consumers. The setup can be extended from a small society to the entire urban infrastructure. The proposed system consists of a waterflow sensor, actuator, microcontroller (ESP8266), and a web-application which can remotely control, cut, monitor, and analyze the water usage. Administrators will have access to restrict the water flow and upon consumers' requests ancillary water can be supplied. Also, this system is equipped with machine learning techniques for predicting the usage and learns the flow patterns of water among different user spaces, which will be useful to estimate the water requirements. Also, classify consumers based on their usage. The application is equipped with a reward system where consumers are rewarded with ancillary water when their consumption is minimal. [ABSTRACT FROM AUTHOR]

The aim of this paper is to carry out work those results in a good friend to farmers in every way. The work primarily recommends a high-yielding crop to the farmer based on regional soil data and the farmer's requirements. The job entails analysing a database of different types of soil from a specific area. To improve the analysis' efficiency, a machine learning method called linear regression is used. The study also implements disease detection in crop leaves using image processing techniques and provides remedy/solutions to farmers with diseased leaves, which promises to be a preventative measure in the future. The study carried out has an effect on various cultivation methods for various crops, assisting farmers as a "SAHA PATI" for this work. This service assists farmers in determining the best method of cultivation based on climatic and geographical factors. The final section of the Paper introduces a programme that provides current market prices for various crops by connecting to a government website that is updated on a regular basis. This service assists farmers in avoiding being duped by business intermediaries. The overall work done with the agriculture-based component turns out to be the best application for the current situation. [ABSTRACT FROM AUTHOR]

Tuberculosis is an infectious disease that spreads through the air when someone sneezes or coughs, and it mainly affects the lungs. It is a very common disease, prevailing mainly in undeveloped and developing nations. Inflammation of the lungs Tuberculosis can be diagnosed with the help of radiology by taking a chest X-ray of the patient and a sample of sputum to check whether bacteria (Mycobacterium) exist or not. The techniques of artificial intelligence can help in the diagnosis and fast predictions of tuberculosis. In this research paper, we analysed the dataset of tuberculosis patents by applying the classification models of machine learning. The accuracy of models will be compared by varying the training and testing datasets. The CPU execution time of the classification models is also calculated, to determine which model performs faster. [ABSTRACT FROM AUTHOR]

Agriculture is the backbone of India which is diversified. Mushroom cultivation is one such farming method that not only gives promising profit to the cultivators but also provides nutritional mushrooms to the consumers. Nowadays, as the demand for the mushroom cultivation is increasing day by day, advanced technologies pave way for the farmers to improve quantity and quality along with its nutritional value of mushrooms. In this paper, pilot study has been conducted in order to extract knowledge-based inference by remotely monitoring the environmental conditions such as temperature and humidity using DHT11 sensor, CO2 using MG811 sensor, rat movement using PIR sensor and light intensity using LDR sensors are monitored through ThingSpeak cloud server to govern the appropriate actuators such as Sprinkler, LED, Humidifier, Exhaust Fan and Buzzer at the given threshold values, along with this Biological Efficiency%(BE%) has been conducted in both manually controlled mushroom and also in IoT enabled mushroom in two types of species i.e., P. florida and P.eous APK1. From the pilot study the results proves that the monitoring and automation of parameters plays a predominate role in producing the nutritious mushroom with increased Biological Efficiency % (BE%) in P.florida as 99.51% in IoT enabled cultivation whereas in manual controlled hut it is 90.99%. The proposed DCE highlights the importance of machine learning algorithm and its collaborative aspects of supervising and governing the environmental conditions in each stage of growth with appropriate actuators and Biological Efficiency %(BE%) which will play a major role in producing the most nutritious mushroom. Therefore, the proposed DCE framework will be greatly helpful for reaping highly nutrient mushrooms and it will help to increase social economic growth of self-help women and agriculturist. [ABSTRACT FROM AUTHOR]

Chatbot is a computer program that mimics human conversation and performs streamlined communication with the user through text messages. The usage of chatbots in the commercial field has been increasing recently due to the enhancement of customer satisfaction and product sales. However, most chatbots depend on antiquated models that can only provide pre-programmed responses, which is unclear and erroneous. This paper aims to build an automated mobile chatbot for a flower shop to assist them in reducing costs, boosting the business's growth, and establishing connections with clients in an increasingly digital environment. The majority of robots have been around for about ten years and work by following rules. These kinds of chatbot answer to customers based on the rules for the conversation. The proposed chatbot is implemented by machine learning approaches and natural language processing algorithms to enhance the chatbot's adaptability and human-like qualities, the chatbot are learning from the unsolved question based on the admin given answer. Besides, the admin interface is created to ease the data management in the proposed chatbot. [ABSTRACT FROM AUTHOR]

Predicting academic performance is a crucial task for educators and institutions because it enables the early identification of at-risk students and helps provide targeted interventions to improve their academic outcomes. Existing research often focuses on predicting academic performance using CGPA; less work, however, has used graduate-on-time (GOT) as a dependent variable. In this study, the objective was to (i) determine the optimal set of features that influence the predictions, (ii) construct a predictive model that predicts academic performance focusing on graduating on time (GOT). The data, obtained from the Ministry of Higher Education Graduates Tracer Study, contains information about graduated MMU students. It has 2382 entries and 95 columns, which include records of Graduate On Time (GOT), Cumulative Grade Point Average, Estimated Terms, and many more. This paper employed machine learning techniques such as Gaussian Naive Bayes, Decision Tree, Logistic Regression, Random Forest, Gradient Boosting, Stacking Ensemble methods and Multilayer Perceptron. The results showed that among all the techniques, the Ensemble method model exhibits the highest accuracy (84.03%), precision (84.86%), and recall (90.57%), as well as a f1-score of 87.62%. The Random Forest model and the Logistic Regression model both have a f1-score of 84%, which comes in second place after the strong results of the Ensemble technique. [ABSTRACT FROM AUTHOR]

Agriculture is a vital industry that contributes to global economic growth and development. But, farmers face challenges, such as crop selection, profitability, and technology, which threaten the sustainability of the agricultural industry. This paper proposes the application of Machine Learning (ML) techniques to address these challenges. The use of ML algorithms, such as Gaussian Mixture with K-Nearest Neighbors (KNN), for Crop Recommendation, where farmers give their soil content and get the crops that can be grown, and Decision Tree algorithm to provide current crop prices, SARIMA to forecast crop prices. This approach utilizes historical crop data and pricing information to train and test the models. Our results indicate that ML can help farmers make right decisions on crop selection, maximize profitability, and optimize pricing strategies. [ABSTRACT FROM AUTHOR]

Orthopedic implant replacement surgery is a procedure that involves the removal of a damaged or diseased joint and the placement of a new, artificial joint. The new joint is made of metal, plastic, or ceramic materials that are designed to mimic the function of the natural joint. Orthopaedic implant replacement surgery is a highly successful procedure that can significantly improve the quality of life for patients who suffer from chronic joint pain. However, as with any surgery, there are risks associated with ortho implant replacement surgery, including infection, blood clots, and nerve damage. This may lead to aseptic loosening which will become the main cause for surgeries to happen for the second time. Therefore, it is crucially important to diagnose it clearly during the pre-operative and post-operative phases. Our main objective is to assist surgeons in identifying these illnesses accurately during the process, to avoid more serious stages. This paper reviews the methods and approaches for Orthopaedic implant replacement surgery using Computer-Aided Design (CAD), Convolutional Neural Networks (CNN), Augmedics, SCORE-CAM and deep learning algorithms. During all the three phases, various technologies are utilized to improve patient wellness. [ABSTRACT FROM AUTHOR]

Post-harvest loss of agricultural products is a significant problem in many developing and expanding countries that are experiencing economic expansion. On the one hand, there is a lack of understanding regarding the sizeable amount of losses, and on the other, there is a lack of knowledge regarding the advantages of applying uncomplicated postharvest and conservation methods. A significant amount of agricultural productivity is lost in many nations. Using the right post harvest technology could help to cut down on food waste. Additionally, it helps to raise product quality, which raises market prices. The most typical way to preserve freshly obtained fruits(grapes) is by drying and storing them. The practice of drying goods outdoors in the sun dates back to ancient times, however there are drawbacks, including a heavy reliance on the weather, slow drying durations, the potential for contamination, etc. These problems may be overcome by using mechanical dryers, although they are energy intensive. Simple mechanical processes and solar-powered drying can lower the moisture content of grapes, enabling longer-term storage. Common methods of raisins processing are the open sun drying and natural air-drying shelter. Both of methods have been long production period, large area, the quality of grapes is low. Mainly traditional solar dryer for grape do not have heat system with lower efficiency. As a result, the drying air condition varies greatly and closely tracks changes in the ambient air condition. The drying process is discontinuous. According to incomplete statistics, the consuming time of grape drying is 15 days with traditional solar dryers. Therefore, the traditional solar dryer is not suitablefor industrial production. the paper mainly dealt with the sustainable self-made solar dryer and heat storage system with nichrome coil. [ABSTRACT FROM AUTHOR]

Oxidative Coupling of Methane (OCM) is a chemical reaction which directly converts natural gas, primarily consisting of methane into value-added chemicals, specifically ethylene. The OCM process faces challenges in the limitations of yield and high separation cost in transforming natural gas into useful chemical products. Ethylene serves various purposes, including the production of fabricated plastics and is widely used as a raw material. This paper focuses on machine learning algorithms to predict ethylene production using a catalysis dataset. So initially the high ethylene yield is predicted from the 18 catalysts collected from the dataset. The clustering algorithm called K-Mean clustering and Agglomerative clustering is used to identify the high-yield ethylene. Among 18 catalysts, 9 elements are identified as the largest amount of ethylene from the catalysis data. The prediction models are used to predict the large yield of ethylene from the catalysis. The prediction model like Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Boosting (GB) are compared to identify the best model for the prediction of ethylene yield. Based on the analysis, the agglomerative clustering along with the Random Forest predicts the higher ethylene composition from the OCM dataset. The ethylene production of the Magnesium (Mg), Barium (Ba) and Zirconium (Zr) chemical compounds along with oxygen and methane is predicted with the error rate of 0.17, 0.89 and 0.99 respectively. [ABSTRACT FROM AUTHOR]

The most frequent cause of natural disasters in India is tropical cyclones. Early warning, real-time monitoring, impact and damage assessment, and relief operations all depend on remote sensing and GIS. The Bay of Bengal is frequently the source of cyclones of various intensities. In this paper, the actual goal is to predicate the tropical cyclone in the tropical region of the Bay of Bengal, for that the main satellite data set is collected from the NASA data repository. The overall work is being done by using the tool MATLAB, where all machine learning classifications are applied to the data set and trained. As a result, which classifier has the highest accuracy will be considered as the best result. The primary goal of this research is to forecast cyclones using day-by-day satellite data of coastal regions and a variety of weather factors. The objective of this work is threefold. First to design a model to reduce the dependency on a single classification technique, secondly to avoid the over-fitting of data, as well as to improve the accuracy of the prediction. Two number of steps in collective learning: Multiple machine learning models created using the same or different machine learning algorithm. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) technology have evolved tremendously over the past few years. Owing to the recent research and development in IoT and M2M communication. Wi-Fi, LiFi, Bluetooth, Cellular technologies and LPWANS are widely used for communication globally, to stay connected, with the main focus to interconnect things, devices altogether allowing people to manage and monitor the behavior of devices from anywhere in the world. Long-range (LoRa) is a communication technology operating in unlicensed frequency spectrum. However, modest data rates, extensively wide range and low-power requirement, makes it favorable technology for majority of the internet of things verticals. LoRa allows End Devices to dynamically select LoRa radio resources in vicinity of LoRa networks. Though, LoRaWAN communication technology is has revolutionized over past few years, most broadly used and fastest growing networks of IoT due to its attractive characteristics like low power consumption, low cost, fast long range bidirectional communication, less bandwidth requirement, License free ISM standard. Clearly, it's a very assuring technology and will be universally accepted in near future, yet we are not witnessing huge IoT deployment, this is, due to issues, such as networking, signal interference, massive deployment, security concerns and concurrent transmission collisions, which need to be addressed effectively. This paper aims at summarizing various wireless technologies, background of LoRa and LoRaWAN, use cases, literature review of the existing solutions to examine recent research works on LoRaWAN performance aspects like networking, scalability, inference, state of art solutions, and classifying the security issues. In addition, a brief summary on, future research directions for potential challenges, problem formulation and proposed methodology based on machine learning approach for dynamic resource allocation, minimize collision and enhance performance of LoRaWAN for effective massive IoT deployment under secure environment, is discussed. [ABSTRACT FROM AUTHOR]

The effectiveness of stochastic gradient methods strongly depends on a suitable selection of the hyperparameters which define them. Particularly, in the context of large-scale optimization problems often arising in machine learning applications, to properly fix both the learning rate and the mini-batch size in the definition of the stochastic directions is crucial for obtaining fast and efficient learning procedures. In a recent paper [1], the authors propose to define these hyperparameters by combining an adaptive subsampling strategy and a line search scheme. The aim of this work is to adapt this idea to both the stochastic gradient algorithm with momentum and the AdaM method in order to exploit the good numerical behaviour of the momentum-like stochastic gradient methods and the automatic technique to select the hyperparameters discussed in [1]. An extensive numerical experimentation carried out on convex functions, with different data sets, highlights that such combined hyperparameters technique makes the tuning of the hyperparameters computationally less expensive than the selection of suitable constant learning rate and mini-batch size and this is significant from the perspective of GreenAI. Furthermore, the proposed versions of the stochastic gradient method with momentum and AdaM have promising convergence behaviour compared to the original counterparts. [ABSTRACT FROM AUTHOR]