Preventing Cyber Attacks using Artificial Intelligence
Exploring Advantages and Challenges of Chatbot Integration in Human-Robot Interaction
Counterfeit Currency Detection using Machine Learning
Artificial Intelligence use in Lung Cancer Screening to Improve Patient Recovery
An Analysis of Various Crypto Coins and their Suitability for Real-Time Applications
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
In recent decades, ubiquitous and pervasive computing has been used to increase people's knowledge awareness in a variety of contexts in healthcare, education, agriculture, and so on. However, these computing systems have significant shortcomings in terms of supporting knowledge sharing, reasoning, machine readability, and sufficient controls over how information about individuals is used and shared with others. Ontology is a semantic web technology that provides a sophisticated knowledge framework for users and systems to share knowledge, represent knowledge, and achieve interoperability and machine readability. This paper discussed linguistic interpretation that may allow more adaptive and optimized solutions for knowledge sharing between domain ontologies, which is required for better communication in pervasive and ubiquitous computing.
Code Smell refers to the telltale signs of poor code design that leads to software quality issues. Developers require specific methods to measure the complexity of Code Smells in order to resolve the problem quickly. Recent research has examined the problem of predicting Code Smell using various detection methods. However, the accuracy of machine learning-based Code Smell detectors is still at a normal level. One of the main objective of this paper is to assess how well dimensionality reduction methods can predict Code Smells. This paper uses three machine learning techniques with feature reduction techniques, such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminate Analysis (LDA). Ten-fold cross-validation is used to ensure that the model is well-trained. Datasets are balanced using the Synthetic Minority Oversampling Technique (SMOTE) to ensure an equal number of classes in each dataset. The experimental result concluded that the AdaBoost method with LDA performs better in both the Long Parameter List and Switch Statement datasets, with an accuracy of 92.72% and 91.24%, respectively.
The development of multimedia technologies is becoming more popular; users are not satisfied with traditional information retrieval techniques. People shared ideas and information with others by using methods of communication such as eye contact, movement, interaction through gestures, verbal and written communication. In data recovery, ranking is a key concept. Despite the importance of highlight choosing, calculations for picking ranking models have been thoroughly explored, but this is not the case for highlight choice. Numerous factor-determination methods used in the grouping are directly applicable to ranking. It recovers the natural color, surface, and shape information of images for effective component extraction using edge identification, which is frequently used in signal processing and picture pressure. It has observed the use of information-digging techniques for finding covered-up designs in the enormous dataset and addressing problems in various scientific domains. This paper inputs maximum number of files, and it has voice-based statements or keywords in it. Voice commands convert the voice into text and start searching the text into a file. This will be done by content-based mining, which will be comparable to word-based mining or data mining, using a hierarchy pattern to decide its priority irrespective of its supportive words.
Human-Computer Interaction (HCI) is the area of intersection between psychology and the social sciences. HCI assists companies in creating technological products accessible to individuals with disabilities. In addition, HCI helps user experience (UX) designers and other professionals understand each user's requirements relating to technology. This paper aims to give an overview of the concept of Human-Computer Interaction. The overview comprises basic definitions, frameworks, goals, and types of HCI. It also includes a voice user interface and sociophonetics in HCI. Finally, Norman's model of interaction and future scope are discussed.
To train a machine learning model using a data record which has multiple properties, is typically a difficult task. The development of over fitting of the susceptible model and the growth of model characteristics are always inversely correlated. Since not all of the traits are always significant, this observation was made, and for instance, several attributes might merely make the data noisier. Techniques for dimensionality reduction are employed to address this issue. In this paper we have also discussed the different approaches and techniques of dimensionality reduction techniques.
