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 machine learning 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 machine learning-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 machine learning 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 machine learning, this system offers a proactive and eff
In the era of digital transformation and increasing concerns regarding data privacy, the concept of Self-Sovereign Identity (SSI) has attained substantial recognization. SSI offers individuals greater control over the...
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In general, machine learning is employed by a number of industries to improve their output. Furthermore, many challenging issues with systems that could include extremely important data are resolved using machine lear...
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In general, machine learning is employed by a number of industries to improve their output. Furthermore, many challenging issues with systems that could include extremely important data are resolved using machine learning (ML) algorithms. This poses a significant risk to systems that depend on machine learning algorithms by making them a target for attackers. Determining a machine learning algorithm's performance and resilience to assaults is crucial for this reason. In this research, three metrics and three datasets—the SMS spam, Liver disease and Heart disease datasets are used to empirically examine the performance and resilience of five machine learning(ML) algorithms against adversarial assault. In this investigation, learning models are developed to evaluate the resilience of K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multinomial Naïve Bayes (MNB) or Gaussian Naïve Bayes (GNB), Random Forest (RF), AdaBoost (ADB). Results are tracked in the SMS spam, Liver disease and Heart disease datasets when it assaults these environments with adversarial tactics. In order to alter training data during hostile attacks, such as assaults that randomly flip labels, employ data poisoning. It has been examined how well each method performs for a particular dataset by adjusting the quantity of tainted data and observing trends in the accuracy rate, f1-score and AUC score values and Evaluation of attack detections. The analysis's findings demonstrate the variability of machine learning algorithm’s results in performance and their resilience to numerous hostile assaults. Furthermore, the impact of an adversarial assault on ML algorithms varies depending on the stage of the attack. Based on the results of the experiment as a whole discussed the evaluation of each type of training dataset detection, the concluded best ML models classification performances and resilience against RLF poisoning attacks are KNN for SMS spam dataset, GNB for liver disease dataset and ADB
Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-en...
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This study employs transfer learning using a fine-tuned pretrained EfficientNetB0 convolutional neural network (CNN) model to accurately detect the various stages of Diabetic Retinopathy. The training process involved...
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Jackfruit is the national fruit of Bangladesh, and one of the most consumed fruits in India, Sri Lanka, Philippines, Indonesia, Malaysia, Australia, and many more countries. The every year due to diseases jackfruit pr...
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Because of the problems of low detection accuracy and long detection time in traditional online learning state detection methods, a new method based on posture recognition is proposed. First of all, a pinhole camera p...
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Interrupted Sampling Repeater Jamming (ISRJ) can produce several false targets through intermittent sampling and forwarding of the intercepted signals. The paper proposes an interference identification and suppression...
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Web APIs are integral to modern web development, enabling service integration and automation. Ensuring their performance and functionality is critical, yet performance testing is less explored due to the difficulty in...
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The development of machine learning has the potential to significantly improve the identification and treatment of pregnancy-related risks in maternal health. This work uses an extensive dataset to create reliable mod...
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