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
Smartphone sensors produce high-dimensional feature vectors that can be utilized to recognize different human activities. However, the contribution of each vector in the identification process is different, and several strategies have been examined over time to develop a procedure that yields favorable results. This paper presents the latest Machine Learning algorithms proposed for human activity classification, which include data acquisition, data preprocessing, data segmentation, feature selection, and dataset classification into training and testing sets. The solutions are compared and thoroughly analyzed by highlighting the respective advantages and disadvantages. The results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 95%.
Perceiving and interpreting color phenomena is a complex process for the human brain. Obtaining the true color of an object requires different experimental, physical, and theoretical results. By examining image features, color, and other aspects, individuals can study techniques for applying full-color images. In this context, color chemistry has been used in crime investigations to detect the time of death. It can be challenging for police to physically examine decomposed dead bodies, especially if they have been unknown for days, weeks, or even months. This makes the task much harder for law enforcement officials. An innovative approach has been developed to assist the police in investigating cases more efficiently. This approach enables them to solve crimes from the convenience of their offices, thereby enhancing their productivity.
The objective of the paper is to mitigate internet negativity by identifying and blocking toxic comments related to a particular topic or product. The detrimental effects of social media abuse and harassment can cause people to refrain from expressing themselves. Although some platforms disable user comments altogether, this method is not efficient. The presence of toxicity in comments can assist platforms in taking appropriate measures. The paper aims to classify comments according to their toxicity levels for future blockage. The dataset comprises comments classified into six types, toxic, severe toxic, threat, obscene, identity hate, and insult. Multiple classification techniques will be employed to determine the most accurate one for the data. The authors will employ four types of classification and select the most precise one for each dataset. This methodology enables the authors to choose various datasets for the problem and select the most accurate classifier for each dataset.
The utilization of online platforms for spreading hate speech has become a major concern. The conventional techniques used to identify hate speech, such as relying on keywords and manual moderation, frequently fall short and can lead to either missed detections or incorrect identifications. In response, researchers have developed various deeplearning strategies for locating hate speech in text. This paper covers a wide range of Deep Learning approaches, encompassing Convolutional Neural Networks and especially transformer-based models. It also discusses the key factors that influence the performance of these methods, such as the choice of datasets, the use of pre-processing strategies, and the design of the model architecture. In conjunction with summarizing existing research, it also identifies a selection of key hurdles and limitations of Deep Learning for discovering hate speech and has proposed a novel method to overcome them. In Bidirectional Long Short-Term Memory and BERT for Hate Speech Detection (BiDETECT), which involves adding a Bidirectional Long Short-Term Memory (BiLSTM) layer to Bidirectional Encoder Representations from Transformers (BERT) for classification, the hurdles include the difficulties in defining hate speech, the limitations of current datasets, and the challenges of generalizing models to new domains. It also discusses the ethical implications of employing Deep Learning to pinpoint hate speech and the need for responsible and transparent research in this area.
In the digital age, people may not be able to live without smart devices because they easily adopt them. IoT has made smart devices possible, which consist of sensors, connectivity, the cloud, and user interfaces. IoT research places sensors at the forefront of smart devices. IoT sensors are efficiently used in different IoT applications to develop smart environments in all fields. These sensors act like human sensing organs that collect real-time information from physical objects or the environment and convert it into electronic signals for processing in the IoT. Normally, two types of networks are applied in IoT: long-range low-power wide area networks and short-range networks. This review aims to assist researchers in understanding IoT applications and protocol classifications using short-range and long-range wireless technology that can communicate wirelessly within regions that facilitate IoT communication from a minimum distance of one millimeter to a few kilometers. This paper presents the types of sensors and various applications of IoT, as well as the energy requirements of different protocols and applications. Each network type has its own unique characteristics regarding energy consumption in the IoT network.
