Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
Resume Screener System
Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
People may express their opinion, attraction, and feelings through social media, which is a basic form of communication technologies. The aim of this paper is to derive different emotion behaviors, which would be used to make a strategic decision. With varying kernels and iterations, Support Vector Machine (SVM) and Random Forest (RF) are used to understand, identify, and compare tourist review results. For these data sets, the results of support vector machines and random forest are compared. The main problems in the analysis of support vector machines (SVM) is kernel selection, which is based on problem of determining a kernel function for a specific task and dataset. In this paper, SVM and RF machine learning approaches are used to analyze tourist sentiment. The effects with various kernels may be fine-tuned by proper parameter collection. The results are better analyzed in order to develop better estimation learning techniques. The proposed work have been tested using Weka machine learning tools. Experiments have shown that using 500 iterations in the 10 Folds Cross Validation testing process, RF has the highest accuracy (91.3182 %) for the dataset used.
Attendance checking is a very important process that is done by most academic institutions to record the presence of students, and yet, the traditional way of attendance checking is likely to be time-consuming. This study aims to determine the efficiency of the QR Code Scanner application method for attendance checking. The specific applications that were used are "QR Attendance Control," paired with "Barcode Scanner," and they were randomly chosen to conduct an efficiency test of the two methods. They were then given questionnaires to assess the efficiency of the respective methods. Five features of attendance checking were used as a basis for a comparison of the two methods. The QR Code Scanner application is favored for its less time-consuming, easy monitoring of absences, and easy compilation of attendance. On the other hand, the traditional method is favored in determining forgery and does not consume a lot of lecture time.
Agriculture in India is a global powerhouse. It is the largest producer of pulses, spices, milk and many other products. Agriculture contributes to 16.5% of India's gross domestic product (GDP), however with constant development of industries and technology, there has been a sharp increase in demand for many grocery products which in turn has led to an increase of price. The problem with India's Agriculture Business is that customers want to buy groceries at a less price and farmers want better price for their products. The main obstacle in this business is the middlemen who buy products from farmers at a lesser price and sell to customers at a higher price. This paper aims to get rid of the middleman and connect the customers directly to the farmers which will benefit both consumers and producers.
Steganography refers to the process of hiding information. The purpose of steganography is to hide information behind images. It means it encrypts the text within the image. Steganography gets completed when communication takes place between sender and receiver. Protection is the most important issue concerned with this. Steganography is used to secure information while it is being transmitted. Before the event of steganography, security is the most important concern for the researchers. Several techniques have been developed to ensure secure transmission. Steganography uses algorithms to hide information behind an image. The information is hidden character-wise behind the pixels of the image. The numerous algorithms or techniques used for steganography are LSB-Hash, RSA Encryption, and Decryption.
Deep Learning and Big Data Analytics are the major focus in current rapidly growing technical environment. The use of large data has become crucial to different organizations as they are collecting huge amount of domain specific data, which contains critical information about cyber security, theft detection, national resources, business economics, marketing and medical information. The assessment of this huge amount of data needs advanced analytical techniques for surveying and predicting future course of action by creating advanced decision-making strategies. Deep learning algorithms utilize the collected training data, to create a representation model. This model uses the computer for predictions or decision making about new data without the need to train the machine explicitly to perform user task. These techniques and algorithms infer high-level complex abstractions as the data are represented through hierarchical process. A major benefit of deep learning is processing and learning from the huge amounts of unsupervised data, analyzing patterns from the data which can be used for big data analytics in which the raw data is largely unlabeled and not categorized. In this paper, deep learning techniques for addressing data of various types or formats is analyzed, enabling fast and full processing of data by integrating large amounts of different information i.e. data transformation is also addressed. It also deals with the quality of data as machine performance improves with the quality of data. Further exploration on the deep learning techniques to assist big data is done by focusing on two key topics on how Deep Learning assist some of the specific problems like Data Variety and Data Quality in Big Data Analytics, and how these techniques can aid in processing the Big Data.
