Sexual harassment is an all-encompassing problem that affects individuals in diverse environments including educational institutions, workplaces, and public areas. Despite increased awareness and advocacy efforts, man...
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Sexual harassment is an all-encompassing problem that affects individuals in diverse environments including educational institutions, workplaces, and public areas. Despite increased awareness and advocacy efforts, many women continue to face harassment daily, especially on the Indian sub-continent, with underreporting and impunity exacerbating the problem. As technology advances, there is a growing opportunity to use innovative solutions to address this problem. In recent years, the Internet of Things (IoT) and machinelearning have emerged as promising technologies for developing systems that can detect and prevent sexual harassment in real-time. This study presents a novel approach for real-time sexual harassment monitoring using a machinelearning-based IoT system. The system incorporates nine force-sensitive resistors strategically embedded in women’s dresses to capture relevant data. It is portable and can be affixed to any type of dressing. If the user wishes to change their attire, the system can be easily removed from the current dress and attached to another dress of choice. This flexibility allows users to adapt the system to suit various clothing preferences and styles. The sensor data are transmitted to the cloud via the NodeMCU, enabling continuous monitoring. In the cloud, a pre-trained machinelearning model, specifically the AdaBoost classifier, was employed to classify incoming data in real time. We applied four ML methods: RF with GridSearchCV, Bagging Classifier, XGBoost, and Adaboost Classifier. The AdaBoost classifier performed best with an accuracy of 99.3% using a dataset prepared by our lab, which consists of 1048 instances and was collected from 50 students. If a sexual harassment event is detected, an alert is generated through a mobile application and promptly sent to appropriate authorities for immediate action to save the victim. By integrating wearable sensors, IoT technology, and machinelearning, this system offers a proactive and eff
This study investigates the potential of machinelearning to predict signal power fluctuations in free-space optical communications, impacted by atmospheric fluctuations. Conducted over a 600-meter terrestrial link, o...
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Well-maintained roads are vital to a country's economy as they provide essential transit routes. Potholes and speed breakers, on the other hand, seriously endanger drivers and reduce safety. Inadequate night visio...
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In software engineering, behavioral state machine models play a crucial role in validating system behavior and maintaining correctness. This paper proposes an extension of an existing architecture for automatically le...
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ISBN:
(纸本)9798350376975;9798350376968
In software engineering, behavioral state machine models play a crucial role in validating system behavior and maintaining correctness. This paper proposes an extension of an existing architecture for automatically learning state machine models of client-server systems that automates processes such as regression detection and test case generation, and guides the development of new features. The learned models help identify potential implementation issues of clients, servers, their interactions, as well as the protocols themselves. The architecture also enhances the debugging process and ensures comprehensive system coverage. By employing the LTSDiff algorithm, the method efficiently detects behavioral changes due to software updates, preventing unintended consequences on system performance. Consequently, the automatically generated state machine models can be used as evidence in security, safety, and reliability assurance, providing a valuable tool for development, testing, and maintenance of complex software systems. The learned state machines and detected changes correctly model the behavior of a client-server system to a specified depth at the level of an active outside adversary with the capability to read, replay, replace, or block any message.
Diabetes is a medical disorder disease that affects our body's food fuel systems. Eating food is converted to glucose and discharged to different body parts through blood and the pancreas releases insulin that hel...
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Nowa days health sector getting more advancement in diagnosis disease in early stages. Early detection and early-stage treatment plays an important role in detection of endometrial cancer in women. However, so many tr...
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Skin cancer is a highly prevalent form of cancer that frequently arises from the proliferation of abnormal cells on the skin. Early detection is crucial for effective treatment and better prognosis. Traditional diagno...
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False Data Injection Attacks (FDIAs) are among the most common cyber threats targeting measurement data and sensor readings in power systems. These attacks pose a significant risk to the integrity of power system data...
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Cancer remains a significant global health challenge, with early and accurate diagnosis crucial for effective treatment. While traditional methods have relied on statistical approaches, the complexity of cancer necess...
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The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this ...
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