Sign Language recognition (SLr) covers the ability to translate Sign Language (SL) signals into written or spoken languages. This technique is useful for hearing-impaired people by offering them an effective method to...
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ISBN:
(数字)9798331540661
ISBN:
(纸本)9798331540678
Sign Language recognition (SLr) covers the ability to translate Sign Language (SL) signals into written or spoken languages. This technique is useful for hearing-impaired people by offering them an effective method to communicate with persons having trouble in recognizing SLs. It can also be employed for generating automatic captions in real-time for live actions and videos. Various models of SLr include machine anddeep learning, and computer vision techniques. A commonly employed technique involves using a camera to capture the body and hand movements of the signer, processing the video data to identify the signs. One of the high tasks of SLr includes the flexibility in SL over numerous individuals and cultures, the complex of definite signs, and the need forreal-time process. This manuscript presents a SL recognition Using an Improved Seagull Optimization Algorithm with deep Learning (SLr-ISOAdL) methodology. The SLr-ISOAdL approach aims to exploit a hyperparameter-tuneddL model to recognize and classify the SLs. In the SLr-ISOAdL approach, a bilateral filtering (BF) approach can be applied to get rid of the noise. For learning andderiving intrinsic patterns, the SLr-ISOAdL approach employs the AlexNet model. Besides, the ISOA can be applied for optimal hyperparameter election of the AlexNet model. Finally, the multilayer perceptron (MLP) technique can be exploited to detect and classify the SLs. The analytical experiment of the SLr-ISOAdL technique is conducted on a benchmark dataset. The investigational analysis highlighted that the SLr-ISOAdL technique gains enhanceddetection outcomes in terms of distinct measures.
This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon class...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540371
This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm forreal-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.
While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the questi...
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The flexibility of implementing edge computing over cloud infrastructures has become essential forresponding to the need forreducing latency and improving data processing in a distributed environment. This research ...
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ISBN:
(数字)9798350350067
ISBN:
(纸本)9798350350074
The flexibility of implementing edge computing over cloud infrastructures has become essential forresponding to the need forreducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing random Forest in the determination of the optimum contribution of resources. The work also set up and appliedreal-worlddatasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast anddoes not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing.
Breast cancer affects women all around the world, and it can be dangerous. As stated by clinical specialists, early detection of cancer saves deaths. Lot of the machine-learning algorithms have been created to categor...
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ISBN:
(数字)9798331512248
ISBN:
(纸本)9798331512255
Breast cancer affects women all around the world, and it can be dangerous. As stated by clinical specialists, early detection of cancer saves deaths. Lot of the machine-learning algorithms have been created to categorize tumors to identify breast cancer, and medical imagery processing is a useful tool for improving classification accuracy. Micro classifications are among the most important indications for early detection of breast cancer. Many investigations have used machine learning methods to address this issue. Numerous classification methods are employed to detect the possibility of breast cancer, either benign or malignant. An image-enhancing technique has been designed to aid in the earlier identification anddiagnosis of cancer. The present research attempts to categorize breast cancer using a machine-learning method. The goal of the current study was to use machine learning (ML) techniques to increase the reliability of disease diagnosis. This study assesses and chooses a number of breast cancer prediction methods based on their accuracy, interpretability, and ease of use.
With the development of data mining technology and the arrival of the era of big data, people can get more and more knowledge and information from the data. However, electronic medical data are stored in isolateddata...
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Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. downsized biomedical sensors are the focus of this article, which explores inn...
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Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence.
The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machin...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machine learning (ML) algorithms for enhanced threat detection. This work intends to improve threat detection systems' accuracy, speed, and adaptability by utilizing ML algorithms' capabilities. The abstract begins by outlining the significance of machine learning in transforming traditional cybersecurity measures. It highlights the limitations of conventional signature-based methods, which often fail to detect novel and sophisticated threats. In contrast, ML-based approaches offerdynamic and proactive defenses capable of identifying previously unseen attack vectors. Furthermore, the paperdelves into the practical implementation of ML models in real-world cybersecurity environments. It covers data collection and preprocessing techniques critical for training accurate andreliable models, as well as the challenges associated with maintaining and updating these models to adapt to evolving threat landscapes. The importance of feature selection and engineering in improving model performance is also emphasized. To validate the effectiveness of ML approaches, the study presents case studies and experimental results from various cybersecurity applications. The abstract concludes by addressing the future prospects and potential advancements in machine learning for cybersecurity. It discusses the role of emerging technologies, such as deep learning and federated learning, in enhancing threat detection capabilities. Additionally, it considers the ethical and privacy concerns associated with deploying ML models in cybersecurity and the need forrobust governance frameworks. In summary, this study presents a thorough analysis of machine learning techniques for enhanced threat detection in cybersecurity, including useful applications, advantages, anddifficulties.
The rapid growth of Internet of Things (IoT) introduces new vulnerabilities, requiring advanced security measures to protect interconnecteddevices and sensitive data. Hence, this paper introduce an innovative Intrusi...
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ISBN:
(数字)9798331538934
ISBN:
(纸本)9798331538941
The rapid growth of Internet of Things (IoT) introduces new vulnerabilities, requiring advanced security measures to protect interconnecteddevices and sensitive data. Hence, this paper introduce an innovative Intrusion Monitoring System (IMS) utilizing deep Belief Networks (dBNs) to address the complexities of IoT network security. The proposeddBNs, is a deep Learning (dL) model which is designed to effectively model and classify complex, high-dimensional IoT data, enabling efficient anomaly detection and pattern recognition. Also, this work incorporates a preprocessing stage, including data cleaning, normalization, and transformation, offering high-quality input for the dL model. In addition, the framework handles dynamic and heterogeneous IoT traffic, ensuring adaptability to evolving threats. The validation of the proposed work using Python demonstrates a detection accuracy of 95.20%, indicates the superior ability of dBNs to learn complex patterns in IoT data. Thereby, the proposed work contributes to proposing a reliable and scalable solution for improving IoT security, supporting the development of next-generation IMS.
Modern finance and Hr businesses have challenges in exchanging datadue to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intellig...
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ISBN:
(数字)9798350360660
ISBN:
(纸本)9798350360677
Modern finance and Hr businesses have challenges in exchanging datadue to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity andresilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of frauddetection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alterdata management methods in finance and Hr industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system.
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