Weapon Detection in Surveillance Videos Using YOLOV8 and PELSF-DCNN

Weapon detection (WD) provides early detection of potentially violent situations. Despite deep learning (DL) algorithms and sophisticated closed-circuit television (CCTVs), detecting weapons is still a difficult task. So, this paper proposes a WD model using PELSF-DCNN. Initially, the input video is converted into frames and pre-processed. The objects in the pre-processed frames are detected using the YOLOv8. In meantime, motion estimation is done using the DS algorithm in the pre-processed images to cover all the information. Then, the detected weapons undergo a sliding window process by considering the motion estimated frames. The silhouette score is calculated for detected humans and other objects. Now, the features are extracted and the important features are selected using the CSBO algorithm. The selected features and the output of YOLOv8 are given to the PELSF-DCNN classifier. Finally, the confidence score is calculated for the frame to define the number of weapons. In an experimental evaluation, the proposed method is found to be more efficient than the existing methods

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

The present article proposes the deep learning concept termed ―Faster-Region Convolutional Neural Network‖ (Faster-RCNN) technique to detect cracks on road for autonomous cars. Feature extraction, preprocessing, and classification techniques have been used in this study. Several types of image datasets, such as camera images, faster-RCNN laser images, and real-time images, have been considered. With the help of GPU (graphics processing unit), the input image is processed. Thus, the density of the road is measured and information regarding the classification of road cracks is acquired. This model aims to determine road crack precisely as compared to the existing techniques

A Comprehensive Survey on Face Quality Detection in a Video Frame

The correctness of the generated face data, which is impacted by a number of variables, significantly affects how well face analysis and recognition systems perform. By automatically analysing the face data quality in terms of its biometric value, it might be able to identify low-quality data and take the necessary action. With a focus on visible wavelength face image input, this study summarises the body of research on the evaluation of face picture quality. The use of DL-based methods is unquestionably expanding, and there are major conceptual differences between them and current approaches, such as the inclusion of quality assessment in face recognition models. In addition to image selection, which is the topic of this article, face picture quality assessment can be used in a wide range of application scenarios. The requirement for comparative algorithm assessments and the difficulty of creating Deep Learning (DL) techniques that are intelligible in addition to providing accurate utility estimates are just a few of the issues and topics that remain unanswered. For each frame, the suggested method is compared to traditional facial feature extraction, and for a collection of video frames, it is compared to well-known clustering algorithms

IoT Based Gas Leakage detection System Using GPS

Gas leaks are a significant problem since they may have disastrous effects on infrastructure, human health, and greenhouse gas emissions, among other things. A method for early detection and alerting of gas leaks is required to reduce these dangers. In this project, we suggest a low-cost and efficient cloud-based Internet of Things (IoT) gas leak detection system for usage in residential, commercial, and industrial contexts. An Arduino Uno microcontroller, a Wi-Fi module, and a MQ 2 gas sensor make up the system. The sensor notifies the microcontroller when gas is detected, and the microcontroller analyses the information before sending it to the cloud through the IoT module. The cloud platform offers a user-friendly interface for managing and visualising data on gas leaks, and it also notifies customers through email and SMS. The system comes with a GPS module and a smoke detector for real-time position tracking and fire detection. The smoke detector detects smoke and sounds an alert, while the GPS module monitors the system’s location. These qualities enable the system to effectively reduce the dangers of gas leaks and fires while enhancing environmental safety

Electricity Consumption Prediction Using Machine Learning

The use of electricity has a significant impact on the environment, energy distribution costs, and energy management since it directly impacts these costs. Long-standing techniques have inherent limits in terms of accuracy and scalability when it comes to predicting power usage. It is now feasible to properly anticipate power use using previous data thanks to improvements in machine learning techniques. In this paper, we provide a machine learning-based method for forecasting power use. In this study, we investigate a number of machine learning techniques, including linear regression, K Nearest Neighbours, XGBOOST, random forest, and artificial neural networks(ANN), to forecast power usage. Using historical electricity use data received from a power utility business, we trained and assessed these models. The data is a year’s worth of hourly power use that has been pre-processed to address outliers and missing numbers. Various assessment measures, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), were used to assess the performance of the models [19]. The outcomes demonstrate that the suggested method may accurately forecast power use. The K Nearest Neighbours(KNN) model outperformed all others in terms of performance, with a 90.92% accuracy rate for predicting agricultural productio

IoT Sensor Based Sustainable Air Quality Monitoring System for Humans and Ecosystems in the World Empowerment

Due to elements that can harm human health, such as industries, urbanisation, population growth, and automobile use, the level of pollution is rising quickly. Using an Internet-connected web server, an IOT-based air pollution monitoring system is employed to track the air quality which sustains environment. When the amount of dangerous chemicals including CO2, smoking, alcohol, benzene, NH3, and NOx is high enough, it will sound an alarm when the air quality drops below a specified threshold. It will display the air quality in PPM on the LCD and on the website, making it very simple to monitor air pollution. The MQ135 and PM 2.5 sensors are used by the system to monitor air quality since they can accurately measure and detect the majority of hazardous gases. In recent years, air pollution has become a severe issue on a global scale and has surpassed advised national limitations. In addition to harming ecosystems and human health, air pollution also has an impact on global climate. The population is expanding, there are more industries, and there is an excessive amount of transportation that uses fuel, which are all contributing factors to the rapid rise in air pollution. To address this danger, the Air Quality Monitoring System was developed

Sustainable Hand Gesture Recognition for Speech Conversion, Empowering the Speech-Impaired

A sustainable language disorder affects an individual’s ability to reach out to others through speaking and listening. So utilizing sustainable hand gestures is among the most widespread means of non-verbal and visual communication used by people with speech disabilities worldwide. However, even though sustainable sign language is used everywhere by speech-impaired and hearing-impaired people, most of the populace who don't have any knowledge about sign language face difficulties in sustainably communicating with them. This sustainable problem requires better solutions that can successfully support communication for people with speech disabilities. This sustainable approach will reduce the communication gap for the speech-impaired population. There are many sustainable solutions in the market such as using sensors to make a sustainable device that gives a helpful output. But these sustainable solutions are expensive and not everyone can afford them. We are employing Convolutional Neural Networks to create a sustainable model that is trained on different gestures. This sustainable model enables speech-impaired individuals to convey their information using signs which get converted to human-understandable language, and sustainable voice is given as output. The sustainable hand gestures made are captured as a series of sustainable images which are processed using Python code. This sustainable endeavor introduces a solution that not only automates the identification of sustainable hand gestures but also transforms them into sustainable speech. By interpreting these recognized sustainable gestures, the corresponding recorded audio will be played sustainably. The focus of this sustainable paper is to offer accessibility, convenience, and safety to individuals with speech impairments in a sustainable manner. These sustainable individuals often experience societal discrimination solely due to their disabilities. This sustainable paper is aimed at innovating a sustainable device to help those without the knowledge of sign language sustainably communicate with the people who face difficulty in speech

Alzheimer’s Disease Recognition Applying Non-Negative Matrix Factorization Characteristics from Brain Magnetic Resonance Images (MRI)

To more accurately depict Alzheimer’s disease (AD) and projecting clinical outcomes while taking into account advancements in clinical imaging and substantial learning, several experts are gradually using ConvNet (CNNs) to remove deep intensity features from gathering images. A small deep learning algorithm called the principal component analysis network (PCA-Net) creates multi-faceted channel banks to verify the accuracy of voluminous head part assessments. After binarization, block wise histograms are constructed to obtain picture properties. PCANet is less adaptable because multi-facet channel banks are built with test data, resulting in PCA-Net features with thousands or even millions of aspects. The non-negative matrix factorization tensor decomposition network, or NMF-TD-Net, is an information-free organization based on PCA-Net that we present in this study to address these issues. Instead of PCA, staggered channel banks are made to test nonnegative matrix factorization (NMF). By applying tensor decomposition (TD) to a higher-demand tensor derived from the learning results, the input’s dimensionality is reduced, resulting in the final image features. The support vector machine (SVM) in our technique uses these properties as input to diagnose, predict clinical score, and categorize AD

DFR-TSD: A Sustainable Deep Learning Based Framework for Sustainable Robust Traffic Sign Detection under Challenging Weather Conditions

The development of reliable and sustainable traffic sign detection under difficult weather conditions, or DFR-TSD, is a key step in creating effective, safe, and sustainable autonomous driving systems. The suggested sustainable framework makes use of deep learning techniques to overcome the drawbacks of the current traffic sign detection systems, especially in difficult weather circumstances like haze and snow. The system uses a sustainable CNN pre-processing step to make traffic signs more visible in photos that have been impacted by the weather, followed by a sustainable pre-trained ResNet-50 model to recognize traffic signs. On the CURE-TSD dataset, which includes difficult weather circumstances such as haze, snow, and fog, the suggested sustainable framework was assessed. The testing findings showed how sustainably well the suggested framework performed in identifying traffic signs in adverse weather. The suggested sustainable framework outperforms previous approaches with a sustainable accuracy rating of 98.83%. The outcomes show that sustainable deep learning methods have the potential to enhance traffic sign identification models' functionality. The proposed sustainable framework’s front end offers a user-friendly interface that enables users to upload test photographs and view the results of the detection. There are four sustainable buttons on the UI for loading the model, uploading test photographs, spotting signs, and seeing the training graph. The Tkinter framework, which offers a user-friendly GUI toolkit that enables developers to quickly design and customize sustainable GUI programs, is used to develop the front end. The suggested sustainable DFR-TSD framework is a potential sustainable option for reliable traffic sign detection in adverse weather due to the sustainable pre-processing step, the sustainable pre-trained ResNet-50 model, and the sustainable user-friendly interface

Deep Learning-based Speech Emotion Recognition: An Investigation into a sustainably Emotion-Speech Relationship

Speech Emotion Recognition (SER) poses a significant challenge with promising applications in psychology, speech therapy, and customer service. This research paper proposes the development of an SER system utilizing machine learning techniques, particularly deep learning and recurrent neural networks. The model will be trained on a carefully labeled dataset of diverse speech samples representing various emotions. By analyzing crucial audio features such as pitch, rhythm, and prosody, the system aims to achieve accurate emotion recognition for novel speech samples. The primary objective of this paper is to contribute to the advancement of SER by improving accuracy, reliability, and gaining deeper insights into establishing a sustainable complex relationship between emotions and speech. This innovative system has the potential to facilitate the practical implementation of emotion recognition technologies across multiple domains