This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods...
This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse iot systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities.
Worldwide, women endure a great deal of unethical forms of domestic violence. The lack of an efficient tracking mechanism is speeding this up. An AI-based system that provides protection to vulnerable women was the su...
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ISBN:
(数字)9798350389449
ISBN:
(纸本)9798350389456
Worldwide, women endure a great deal of unethical forms of domestic violence. The lack of an efficient tracking mechanism is speeding this up. An AI-based system that provides protection to vulnerable women was the subject of this research. In critical situations, this framework can respond appropriately automatically and manually. No place is secured for women, but they are more at risk when venturing out on their own into remote areas and unfamiliar roads. After recognizing danger, current women’s handheld safety gadgets require human involvement to activate (e.g., pushing a button or shaking the device). We provide an approach that aims to provide women with false proof safety while also attempting to address the shortcomings of current systems. This system showcases an Arduino-powered wearable safety gadget designed specifically for women. This gadget is designed to protect ladies from harm in case they encounter any risk. The gadget is able to converse and deliver alerts using a wireless sensor network. The user’s precise whereabouts can be sent to designated authorities and stored contacts through the use of GPS and GSM. In the event of an emergency, the device’s switch can be used as a panic button to get a shock; when pressed, the buzzer and laser diode will both activate. This research presents a novel approach to women’s safety, the iot Powered Women Protection (iotPWP), and evaluates its efficacy by cross-validating it with the usual model, the bluetooth enabled Women Safety System (bWSS). We also believe that shows with built-in cameras that record electric shock and broadcasting real-time footage can have an effect on women’s lives through the medium of digital technology.
Semantic Index, Human Action Detection, and Event Detection are video surveillance packages that assist automate surveillance tasks. Video surveillance structures have entered the generation of virtual surveillance st...
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Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs ...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises.
In order to develop strategic initiatives in agricultural belonging for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computable and commercial deci...
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In order to develop strategic initiatives in agricultural belonging for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computable and commercial decision at the territory plane. Crop production forecasting, which is done to foresee a greater crop output, is the impressive difficulty in the agronomic division. To help farmers increase agricultural yield, this study has collected and evaluated data on N, P, K, temperature, humidity, ph, rainfall and soil_moisture. The superior values of this work are to recommend the crop to cultivate, suggest the appropriate fertilizers to use and predict the disease of a crop. A python environment is used to perform initial preprocessing on the data. KNN classification is used for developing the recommendation system. The link between the count, nutrients, temperature, humidity, pH, rainfall, and crop, are visualized using bar graphs and scatter plots. The tensor and torch were used to forecast crop disease and also to suggest appropriate fertilizer based on the data. The application was developed using Python Flask. This Classification was compared with other algorithms such as SVM, DT , RF and K-means.
The quantity of Municipal Solid Waste (MSW) gets intensified, based on various factors such as population growth, monetary status and consumption patterns. The insufficiency of elementary trash data is a critical prob...
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In image-guided liver surgery, the registration of preoperative reconstructed liver model with intraoperative captured surfaces is extremely challenging, due to the limited visibility of liver surfaces and the impact ...
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Metastasis is the main factor contributing to death in breast cancer patients. Faster and more accurate deep learning algorithms are being researched as potential replacements for the current, labor-intensive techniqu...
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ISBN:
(数字)9798331541583
ISBN:
(纸本)9798331541590
Metastasis is the main factor contributing to death in breast cancer patients. Faster and more accurate deep learning algorithms are being researched as potential replacements for the current, labor-intensive techniques used to diagnose metastases from lymph nodes. For testing and training purposes, a total of 220025 whole-slide pictures representing the lymph nodes of two cohorts of patients were identified. In order to detect metastatic cancer, we used a hybrid convolutional neural network model. A total of 57458 unlabeled images were utilized in order to verify the accuracy, sensitivity, specificity, and P-value of our diagnostic method. The DL-based approach was created to automatically and selectively assess and identify metastasizing lymph nodes. In the quantification procedure, accuracy was 98.84%. Moreover, the accuracy rates for VGG16 and Recall were 92.42% and 91.25%, respectively. The differentiation levels of metastatic cancer may have an impact on recognition performance, according to later study. Our devised diagnostic complex showed a high degree of efficacy and accuracy for lymph node diagnosis. Patients with breast cancer may find it simpler to perform pathological screening for metastases thanks to our innovative DL-based method.
Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social m...
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technological advances such as iot have provided a foundation for smart environment that are capable of self-adjustment to the needs of users. As the consumption of resources becomes faster and faster, control of ener...
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