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
The rapidly spreading COVID-19 pandemic in 2020-2021 affected more than 190 countries, including Nigeria. Following this scenario, countries around the world were monitoring confirmed cases, recovering and dying. In order to reduce the impact of the pandemic, most countries implemented several measures to control the spread of the virus. These include closing schools and borders, shutting down public transport and workplaces, and restricting public gatherings until herd immunity is achieved through vaccination. The breakdown of the health system and the unpredictable nature of human behaviour makes it difficult to predict and evaluate the impact of lockdown on the COVID-19 pandemic in the long term. In light of the above, this study developed Hybrid ANN-CNN and four other models, namely LASSO, ANN, CNN and LSTM, to predict and evaluate the effect of human mobility on COVID-19 confirmed cases. To evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and to predict the spread of COVID-19 confirmed cases, publicly availabledata on human mobility collected by Google and air passenger data were used. In this study, our motivation was to evaluate effect of lockdown on COVID-19 and models that predict the impact of human mobility on COVID-19 confirmed cases based on MSLE, Huber loss, and Log Cosh performance measures. At the end of the experiment, the developed hybrid ANN-CNN outperformed the other four models with MSLE of (0.0022), Huber Loss (0.0014) and Log Cosh (0.0013) respectively. This study serves as an alarm system to provide policy makers with the human mobility factors that can trigger large numbers of cases during a pandemic. This will allow for urgent public action.
Computer Networks and the internet are essential to our daily lives and enterprises. DoS assaults threaten computer networks and network security. The world is evolving toward online businesses and services. This has increased network traffic over time. We need NIDS and DoS attack detection since there are more network risks and attacks. DoS attacks now threaten computer network servers. This threat must be detected automatically to protect corporate assets. Anomaly-based intrusion detection was developed because signature-based DoS attack and intrusion detection methods are inadequate. Many studies employ Machine Learning and Deep Learning to detect network anamolies. This article describes classification models constructed with the aid of machine learning algorithms. On the own dataset, this research was performed utilizing machine learning algorithms including K-Nearest Neighbor (KNN), Logistic Regression, and Random Forest. Random Forest outperforms other supervised machine learning algorithms, as demonstrated by this study's findings. It achieved an accuracy rate of 99.62% when nine features were selected utilizing Pearson's correlation coefficient method. The own dataset file (myNetworkGenerateTraffic.csv) which was captured through wireshark tool were utilized to accurately evaluate machine learning algorithms. We utilized the following performance metrics in this investigation: Accuracy, Precision, Recall, and F-1 score. In this paper, we examine how machine learning techniques can improve DoS attack prediction in network traffic to better analyze network traffic and help improve network security.
The Agriculture Management System is a web-based platform that provides farmers with a full range of agricultural information. Its goal is to increase farmers' productivity and profitability by using a centralized system for managing their agricultural operations. The system gives farmers access to critical data such as weather forecasts, soil and crop statistics, allowing them to make informed decisions and improve their farming operations. In terms of weather management, the technology provides precise forecasts, which help farmers plan their operations more efficiently. Farmers can use this data to identify the best time to plant, harvest, and perform other agricultural duties. Regarding market prices, the AMS gives farmers with real-time crop and livestock prices, allowing them to make informed pricing and marketing decisions. Furthermore, the technology provides farmers with critical soil and crop data, giving them insights into soil quality and crop health. This data helps farmers improve their agricultural practices by allowing adjustments to planting schedules, fertilizing, and irrigation systems to increase crop yields and quality. Recognizing the value of cooperation and information sharing, the AMS includes an AI-powered chatbot. This feature allows farmers to share information, seek assistance, and ask questions about the agricultural management system, including crops, soil, market trends, and weather management. The incorporation of this interactive technology encourages a communitydriven approach to agricultural management, creating a conducive atmosphere for farmers to flourish.
In this study, we delve into sentiment analysis and the role of Explainable Artificial Intelligence (XAI), with a focus on techniques such as Lime that bring transparency to machine learning (Logistic Regression) and deep learning (LSTM) models. We explore how ML predictions can be biased using XAI and how XAI helps us understand DL models used in sentiment analysis through research that has been made. Examining various research, we notice a gap – the lack of training and interpretation for both ML and DL models on the same dataset using XAI. Our research fills this gap, shedding light on ML and DL model predictions through XAI's lens. By completing our research work, we come to know that even with an accuracy level of 83% for the DL model, they outperform the ML model with an accuracy level of 92% in some cases. This distinction is only identified with XAI techniques, particularly Lime.
This research paper introduces a state-of-the-art "Resume Screener System" aimed at revolutionizing and automating the labor-intensive task of resume analysis for recruitment purposes. Developed using Python, the system integrates Artificial Intelligence and Natural Language Processing techniques to streamline the hiring process. Utilizing a dataset sourced from Kaggle, comprising a thousand resumes converted into textual data, the system undergoes comprehensive model training and evaluation. Employing advanced machine learning methodologies such as the Support Vector Classifier (SVC) and Neighbours Classifier, the system rigorously tests and analyzes these models to determine the most effective approach. By evaluating each model's performance against predefined criteria, the system identifies the optimal model for resume screening. The primary objective of this work is to provide recruiters and HR professionals with an innovative tool that efficiently matches job requirements with candidates' skill sets as presented in their resumes. By automating the initial screening phase, the system not only saves time and effort but also ensures a more objective and consistent evaluation of applicants. This research contributes to the advancement of machine learning applications in the field of human resources, illustrating the transformative impact of technology on traditional hiring practices.
