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

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

An Efficient, Novel, and Sustainable IoT-Based Approach for Attendance Detection through RFID Module and IR Sensor

The maintenance of student attendance at a facility is a challenging endeavor. The attendance is manually inputted, making it easily manipulatable. To effectively monitor and control student attendance in a setting, this research intends to build an intelligent attendance system with a sustainable approach to the problem. This study indicates employing RFID and IR sensors to automatically take attendance. Data can be viewed and stored in a real-time database using RFID (Radio Frequency Identification). The firebase database/google sheets are updated when the student’s card is brought close to the RFID module. Additionally, an IR sensor is employed to keep track of the overall number of students entering and leaving the classroom. It would be useful to know how many students are in the class overall, and how many students are in the class overall would be displayed on the LCD outside the class. The outcomes of the research on student attendance will be communicated to the parents, and the number of students in attendance overall will be forwarded to the higher representatives of the institutions. The system alerts the user if there is any discrepancy between the number of RFID tags scanned and the total number of students present in the classroom. The teacher must manually rectify the attendance error after the disparity in the count reveals the proxy in attendance. Future expansion of this research may include the use of biometrics and face recognition to detect the proxy and open up the possibility for a more sustainable environment

An efficient novel approach to E-commerce retail price optimization through machine learning

Businesses can use price optimization to discover the most profitable price point by using customer and market data to drive their decisions. The optimal price points will result in the company making the most money possible, but they may also be created to help the company expand into untapped markets or increase its market share, for example Businesses can use machine learning to price products and services to maximise sales or profitability by using data instead of educated guess-work. When utilised for price optimization, ML-based algorithms can be used to forecast demand for a particular product as well as the ideal price and how buyers will respond to specific pricing. Pricing decisions can be made more accurately using machine learning, which will boost a company's revenue

Image-based Driver Alert System for Prevention of Fatigue-related Accidents

The objective of this project is to design a driver unconsciousness detection system using image processing to detect drowsiness and unconsciousness in drivers, thereby preventing accidents resulting from driver fatigue. Driver fatigue is a serious road safety issue, with approximately 20% of all road accidents attributed to this cause. Conventional drowsiness detection systems rely on physiological monitoring, which can be unreliable, expensive, and challenging to implement and maintain. In contrast, the proposed system monitors a sequence of images to identify facial and behavioral patterns indicative of drowsiness or unconsciousness. By detecting facial landmark points and analyzing the duration of eye closure, the system can accurately classify the driver’s state and take appropriate measures such as reducing the vehicle’s speed and alerting emergency services of the driver’s geo-location. The successful implementation of this system holds immense potential for substantially reducing the number of accidents resulting from driver fatigue, thereby mitigating the loss of lives and injuries