Search

This paper proposes a semi-automatic method of geographic information linking based on spatial relationships, entity names, entity categories and other features, combined with semantic and machine learning methods. First, we extracted geographic information from three geographic information sources: Open Street Map, Wikimapia, and Google places. The extracted geographic information is mainly for urban buildings in different regions. Secondly, we analyzed and extracted the characteristics of geographic information data to construct a geographic information ontology, and realized the integration of geographic data through the mapping of geographic information source data and geographic information ontology. Finally, the linking method of fusion classification algorithm support vector machine and K-nearest neighbor method are discussed separately. At the same time, it is compared with the linking method proposed by Samal et al. to comprehensively verify the accuracy of the method proposed in this paper from multiple angles, laying a good foundation for seismic information integration. [ABSTRACT FROM AUTHOR]

The capacity to identify breaches and malicious activity inside the Internet of Things (IoT) networks is important for network infrastructure resilience as the dependence on IoT devices and services grows. Intrusion detection systems (IDS) are basic components of network security. IDSs monitor and analyze the activity of a system in a network to identify intrusions. Existing intrusion detection systems (IDS) gather and utilize large amounts of data with irrelevant, unnecessary, and unsuitable characteristics, resulting in long detection times and low accuracy. In this paper, we present an IDS model based on a Random Forest (RF) classifier. NSL-KDD dataset is used to test the performance of the model and the satisfying performance is obtained in terms of accuracy, detection rate, and false alarm rate. The proposed model has attained an average accuracy of 99.3% and 98% for binary classification and multiclass classification, respectively. To demonstrate the efficacy of the suggested model, its accuracy was compared with some existing approaches that utilize other models such as AIDS, ELM and PCA, MapReduce-based hybrid architecture, and DRNN. [ABSTRACT FROM AUTHOR]

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers' performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program's quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor's performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor's performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods. [ABSTRACT FROM AUTHOR]

In the digital world of today, any enterprise that deals with the amounts of data in Warehouse Management Systems (WMS) are an important component. Furthermore, the amount of data being raisedand its complexity have become more challenging to maintain the WMS efficiency. Therefore, a device is required, which can manage such complexities autonomously with no human intervention. In this paper, Hybrid Machine Learning with the Internet of Things (HML-IoT) improves isolated doors. Furthermore, operating machine performance in the factory of hazardous goods. Decision-Making Algorithm (DMA) Data from the customer's holding space's dangerous goods warehouses shall be checked using separated doors. This paper's significant aspect is that inventory and inventory operation's organizational performance can be increased, further logistics costs minimized utilizing the fair use of isolated doors. Finally, the HML-IoT model integrated hazardous goods warehouse with isolated doors has been contrasted with the current one, demonstrating that the previous one has greater efficacy. [ABSTRACT FROM AUTHOR]

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]

The fifth-generation (5G) technology is anticipated to permit connectivity to billions of devices, called the Internet of Things (IoT). The primary benefit of 5G is that it has maximum bandwidth and can drastically expand service beyond cell phones to standard internet service for conventionally fixed connectivity to homes, offices, factories, etc. But IoT devices will unavoidably be the primary target of diverse kinds of cyberattacks, notably distributed denial of service (DDoS) attacks. Since the conventional DDoS mitigation techniques are ineffective for 5G networks, machine learning (ML) approaches find helpful to accomplish better security. With this motivation, this study resolves the network security issues posed by network devices in the 5G networks and mitigates the harmful effects of DDoS attacks. This paper presents a new pigeon-inspired optimization-based feature selection with optimal functional link neural network (FLNN), PIOFS-OFLNN model for mitigating DDoS attacks in the 5G environment. The proposed PIOFS-OFLNN model aims to detect DDoS attacks with the inclusion of feature selection and classification processes. The proposed PIOFS-OFLNN model incorporates different techniques such as pre-processing, feature selection, classification, and parameter tuning. In addition, the PIOFS algorithm is employed to choose an optimal subset of features from the pre-processed data. Besides, the OFLNN based classification model is applied to determine DDoS attacks where the Rat Swarm Optimizer (RSO) parameter tuning takes place to adjust the parameters involved in the FLNN model optimally. FLNN is a low computational interconnectivity higher cognitive neural network. There are still no hidden layers. FLNN's input vector is operationally enlarged to produce non-linear remedies. More details can be accessed application of Nature-Inspired Method to Odia Written by hand Number system Recognition. To validate the improved DDoS detection performance of the proposed model, a benchmark dataset is used. [ABSTRACT FROM AUTHOR]

In human-computer interaction and virtual truth, hand pose estimation is essential. Public dataset experimental analysis Different biometric shows that a particular system creates low manual estimation errors and has a more significant opportunity for new hand pose estimation activity. Due to the fluctuations, self-occlusion, and specific modulations, the structure of hand photographs is quite tricky. Hence, this paper proposes a Hybrid approach based on machine learning (HABoML) to enhance the current competitiveness, performance experience, experimental hand shape, and key point estimation analysis. In terms of strengthening the ability to make better self-occlusion adjustments and special handshake and poses estimations, the machine learning algorithm is combined with a hybrid approach. The experiment results helped define a set of follow-up experiments for the proposed systems in this field, which had a high efficiency and performance level. The HABoML strategy decreased analysis precision by 9.33% and is a better solution. [ABSTRACT FROM AUTHOR]

Heart disease kills more people around the world than any other disease, and it is one of the leading causes of death in the UK, triggering up to 74,000 deaths per year. An essential part in the prevention of deaths by heart disease and thus heart disease itself is the analysis of biomedical markers to determine the risk of a person developing heart disease. Lots of research has been conducted to assess the accuracy of detecting heart disease by analyzing biomedical markers. However, no previous study has attempted to identify the biomedical markers which are most important in this identification. To solve this problem, we proposed a machine learning-based intelligent heart disease prediction system called BioLearner for the determination of vital biomedical markers. This study aims to improve upon the accuracy of predicting heart disease and identify the most essential biological markers. This is done with the intention of composing a set of markers that impacts the development of heart disease the most. Multiple factors determine whether or not a person develops heart disease. These factors are thought to include age, history of chest pain (of different types), fasting blood sugar of different types, heart rate, smoking, and other essential factors. The dataset is analyzed, and the different aspects are compared. Various machine learning models such as K Nearest Neighbours, Neural Networks, Support Vector Machine (SVM) are trained and used to determine the accuracy of our prediction for future heart disease development. BioLearner is able to predict the risk of heart disease with an accuracy of 95%, much higher than the baseline methods. [ABSTRACT FROM AUTHOR]

The article offers information on modern art design system based on the deep learning algorithm. It discusses challenges face by an art teacher which is explaining the meaning behind the art that is sometimes deemed meaningless or shocking, various journals in creative disciplines, addressed the age of painting as of aesthetic and also discussed introduction of artificial intelligence in creative art design.

The number of autistic children and young people is rising rapidly across the world. Children with intellectual disabilities need special attention from trained experts. Educating them on improving their lifestyle is critical through the traditional teaching-learning environment. This study introduces an interactive educational framework that helps children with special needs have an improved and exciting learning process and explores the need to incorporate physical exercise into their everyday lives. Virtual Reality (VR) seeks more attention from autistic students. This research presents a Machine Learning-based Virtual Reality Application (ML-VRA) for Mentally Challenged Children and keeps the Human Behavior Analysis log files. Machine learning can predict the score of brain data ability. The visual short-term memory and visual-spatial memory are further assessed to identify students' interaction with the VR application. Support Vector Regression prediction algorithm and Baseline Prediction algorithm are used to assess the score prediction for visual short memory and visual-spatial memory. Using an audio technology that allows autistic persons to hear various sounds, the cognitive method VRA instructs autistic children. Further, this study proposes a cognitive model for intellectual task processes and problem-solving using metacognitive architecture. Thus, children can acquire different levels of learning knowledge and skills. The case study performed on these model results with the highest prediction accuracy of 93.65%. [ABSTRACT FROM AUTHOR]

With the deepening of China's reform and opening up, people's living standards have been continuously improved, which has promoted the rapid development of our country's culture and arts, such as music. As an important part of music, dance plays an important role in the culture and art of our country. People can use dance gestures to express moods or inner feelings that cannot be expressed in words. In order for the dance to be received and favored by people, it is necessary to have better ideas to express emotions, which is related to the choreographer of the dance. This article takes choreographer as the research object, uses the music feature extraction technology in machine learning to the choreographer, applies the extracted music feature information to the choreographer, and synchronizes the audio and video to achieve better dance effect, which has important research value. [ABSTRACT FROM AUTHOR]