Evaluating the Effectiveness and Challenges of the Solid Waste Management System in Lilongwe City Council, Malawi
Posture and Stress Detection System using Open CV and Media Pipe
City Council Help Desk Support System
DDoS Attacks Detection using Different Decision Tree Algorithms
Comprehensive Study on Blockchain Dynamic Learning Methods
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
Social media is a useful platform that facilitates the sharing of information and builds a virtual network among communities. This platform is sometimes useful to connect people and share useful information among friends and relatives. Nowadays, this platform is also used to share skills through short videos, transfer payments through the Unified Payments Interface (UPI), advertise and promote products to enhance businesses. However, social media platforms are connected through public networks, so a lot of hackers are connected to the network. The hackers want to steal personal information or manipulate the views of the users. Therefore, it applies various social engineering activities to gather personal information and spread a lot of fake news through various channels, apps, and social media pages. Therefore, it is a great challenge to detect fake news. Currently, many research communities are working to implement an algorithm that can detect fake news automatically based on an analysis of data. In an analysis of data, machine learning approaches such as regression, classification, and clustering methods may play an important role in detecting fake news from various datasets obtained from social media sites or the internet. In this paper, a deep analysis of combined datasets for fake news detection has been performed and this analysis is based on machine learning approaches. In addition, it compares the performance of machine learning models such as logistic regression, support vector machines, random forests, passive-aggressive classifiers, and decision trees.
In recent years, there has been an increase in online education. However, an adequate technique was not developed for online examinations. Some educational facilities have compiled assignments that students may copy and paste from the web, while others use remote proctoring, where a human proctor monitors what the students are doing online. Cheating in online tests remains common despite all the growth that has taken place in this field. This paper proposes an Artificial Intelligence (AI)-based system that can assist in the automatic detection of fraud in online tests. This method is both efficient and trustworthy. This system outperformed previous systems in experiments.
This paper offers a concise analysis of the strategies currently in use for stock price prediction by retail investors. The price may increase or decrease according to the quarterly results, financial news flow, technical behavior, or market sentiment resulting from recent developments worldwide. This paper discussed the accuracy of many proposed approaches and methodologies for predicting stock price movement. The Support Vector Machine (SVM) is the foundation of the approaches, with additional parameters and variables.
Due to the Corona Virus Diseases (COVID-19) pandemic, education is completely dependent on digital platforms, so recent advances in technology have made a tremendous amount of video content available. Due to the huge amount of video content, content-based information retrieval has become more and more important. Video content retrieval, just like information retrieval, requires some pre-processing such as indexing, key frame selection, and, most importantly, accurate detection of video shots. This gives the way for video information to be stored in a manner that will allow easy access. Video processing plays a vital role in many large applications. The applications required to perform the various manipulations on video streams (as on frames or say shots). The high definition of video can take a lot of memory to store, so compression techniques are huge in demand. Also, object tracking or object identification is an area where much considerable research has taken place and it is in progress.
Due to its numerous practical applications, human emotion recognition from speech is now a challenging and demanding research subject for scientists. The Speech databases, speech features, and classifiers are the important factors for recognizing emotions from speech. The availability of suitable emotional speech databases is the first step for Speech Emotion Recognition (SER). This paper presents a comprehensive literature review of emotional speech databases. The availability of appropriate emotional speech databases in all emotions and languages are summarized. A total of 26 papers for the emotional speech database have been reviewed. It determines which speech databases are often used and also identifies popular speech databases and the languages in which the majority of the databases are available.
