The buzz surrounding artificial intelligence has made machine learning a hot topic right now. Although there are many tools for visualising data, scripting languages are primarily used for model training. The Orange D...
The buzz surrounding artificial intelligence has made machine learning a hot topic right now. Although there are many tools for visualising data, scripting languages are primarily used for model training. The Orange data Mining tool gives you a lot of ways to change how data is preprocessed, how it is displayed, how models are trained, and how models are tested. In order to establish which strategy has the best Classification Accuracy and Precision, the proposed research uses machine learning techniques to predict the size of an organization based on a variety of parameters, including employee experience, income, job type, employee type, etc. The effectiveness of various machine learning techniques, including Naive Bayes, random forests, support vector machines, neural network, logistic regression, was evaluated. Classification Accuracy evaluations are performed by cross validation. For this paper, we consulted the Kaggle dataset.
Traditional ceramic tile defect detection methods are often limited by the finite nature of feature representation and the influence of complex backgrounds, resulting in low model accuracy and generalization ability. ...
Traditional ceramic tile defect detection methods are often limited by the finite nature of feature representation and the influence of complex backgrounds, resulting in low model accuracy and generalization ability. This paper proposes a Shuffle Attention Mechanism-based YOLOv5 detection algorithm, aiming to enhance the accuracy and robustness of ceramic tile defect detection. It is compared and analyzed against the YOLOv5 algorithm, as well as improved YOLOv5 algorithms based on three different attention mechanisms: SimAM, GAM, and NAM. Experimental results demonstrate that this algorithm effectively captures crucial features related to defects in ceramic tile images, enabling more accurate detection of various types of ceramic tile defects and reducing false positives. In summary, this study demonstrates the effectiveness and potential of the Shuffle Attention YOLOv5 algorithm in ceramic tile defect detection.
Social Networks are integral part of our lives. Each of them has application programming interface (API) to access its data. People willingly store their private information, photos, thoughts and locations in numerous...
Social Networks are integral part of our lives. Each of them has application programming interface (API) to access its data. People willingly store their private information, photos, thoughts and locations in numerous forums. We decided to determine how much information we could obtain about a person using automatic cloud serverless architectures, social network’s APIs and advanced data Science models and algorithms. The paper shows how vulnerable privacy is and how easy it is to consolidate users’ information using modern cloud technologies. We were able to obtain geolocations, friends, similar profiles, predict influencers, and even predict missing friends using Machine Learning graph models. At the end of the day, people should always guard their private information and innovative social platform corporations should carefully think about what data could be given to third parties.
In this study, a modified class topper optimization algorithm named as MCTO is proposed for clustering problem. In the MCTO, cloning, crossover and mutation operators are introduced for global and faster convergence a...
In this study, a modified class topper optimization algorithm named as MCTO is proposed for clustering problem. In the MCTO, cloning, crossover and mutation operators are introduced for global and faster convergence and better quality solutions. Two different types of hybridizations with K-means named as K-MCTO and MCTO-K are then performed with the MCTO to check the effectiveness. First, local optimal solutions obtained from the K-means is fed to the MCTO to get a global optimal solution. Then, another hybridization is done by feeding the solutions obtained from the MCTO to the K-means. Experimental study shows that the MCTO, K-MCTO and MCTO-K outperform various well-known state-of-the-arts. The algorithms are validated by clustering five standard data sets taken from agriculture, health-care and geology domains.
The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker c...
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The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability.
Malware is intended to harm computers or networks, and it frequently entails engaging in unlawful or forbidden activity that can be utilised for espionage or financial gain. Malware assaults are now beginning to affec...
Malware is intended to harm computers or networks, and it frequently entails engaging in unlawful or forbidden activity that can be utilised for espionage or financial gain. Malware assaults are now beginning to affect embedded computational platforms, including Internet of Things (IoT) devices, medical equipment, and environmental and industrial control systems. This research propose novel technique in web user data analysis for behavioural artifacts detection using machine learning (ML) architectures. Here web user access data has been collected and processed for noise removal and smoothening. Then this data feature is trained and selected for detection of malware activities using attribute ratio rule based auto encoder training. Then the selected data is classified using ensemble of spatio temporal Q-learning architectures. the experimental analysis is done for various dataset in terms of accuracy, F_measure, detection time, mean average precision, processing time, specificity. Proposed technique attained accuracy of 97%, F_measure of 86%, detection time of 75%, mean average precision of 63%, processing time of 77%, specificity of 85%.
Accurate analysis of Side Scan Sonar (SSS) images has potential applications in the identification of submerged objects like ships, planes etc. The underwater SSS images are inherently contaminated with noise. In this...
Accurate analysis of Side Scan Sonar (SSS) images has potential applications in the identification of submerged objects like ships, planes etc. The underwater SSS images are inherently contaminated with noise. In this manuscript, a bilateral filtering algorithm is applied for noise reduction. After the noise reduction, the objective is to segment the image and determine the object, shadow, and background. The image segmentation is dealt here as a multi-objective clustering problem. Minimization of intra-cluster distance and negative of inter-cluster distance are taken as the two objective functions for analysis. The optimization task is carried out by four benchmark multi-objective optimization algorithms SMS-EMOA, RVEA, MOEA/D, and NSGA-II. Simulation study on four KLSG SSS Images reveals that the performance obtained by the SMS-EMOA algorithm is superior compared to its counterpart algorithms.
artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the ...
artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation.
We develop a machine learning-based pedestrian detection and alert system that can operate both during the day and at night using a visual camera, an infrared camera, and a radar sensor. The visible camera is used to ...
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We develop a machine learning-based pedestrian detection and alert system that can operate both during the day and at night using a visual camera, an infrared camera, and a radar sensor. The visible camera is used to detect pedestrians during the daytime while the infrared camera during the nighttime. Whereas the radar sensor is utilized to detect the presence of pedestrians including their range and directions of motion. We have developed and conducted actual experimentation of the system in a vehicle. We achieved an average accuracy of 98% based on our proposed multi-sensor data analysis using a deep learning algorithm that classifies a pedestrian’s presence during the day and at night and alerts the driver in a real-time monitoring system.
The design and implementation of efficient routing architectures is a critical aspect of modern communication systems. This paper proposes a modified VLSI-based router architecture that is optimized for high-speed dat...
The design and implementation of efficient routing architectures is a critical aspect of modern communication systems. This paper proposes a modified VLSI-based router architecture that is optimized for high-speed data transfer and low power consumption. The proposed architecture utilizes advanced routing algorithms and state-of-the-art VLSI design techniques to achieve a high level of performance and scalability. The performance of the design is evaluated through simulations. The simulation was carried out in a software called Xilinx and it is written using VHDL language. Design contains blocks called Arbiter, Cross bar and FIFO. The results show that the proposed architecture is able to achieve high throughput while maintaining a high level of scalability. This work is a significant step towards the development of high-performance communication systems.
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