MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

The fusion of normalized difference vegetation index (NDVI) and land surface temperature (LST) provides very important information for analyzing systems such as agricultural drought monitoring system or drought early warning system. This research paper focuses on the study of the relation amongst land surface temperature and normalized difference vegetation indices using time series composed of Landsat 8 satellite OLI/TIRS sensor data. For deriving LST, the Radiative Transfer Equation (RTE) algorithm and the Planck’s function have been used with a PyQGIS plugin of Landsat 8 TIRS data. Along with LST and (NDVI), at-sensor reflectance, Land Surface Emissivity (LSE) and brightness temperature are also calculated using the PyQGIS plugin. TIMESAT software is used for estimation of phenological parameters which helpsin understanding the growth of crops at different stages, effects of climate changes on crops and finding the relation between LST and NDVI. It has been observed that LST and NDVI are strongly negatively correlated. Planck’s function gives the best negative correlation result as compared to the RTE algorithm. Also, various adjustment methods such as Savitzky Golay and Double Logistic Function are used for computation of seasonality parameters and it can be observed that Double logistic gives results.

School of Electronics & Communication Engineering

Engineering and Technology

Journal of Xidian University

Normalized Difference Vegetation Index and Land Surface Temperature Evaluation of Agricultural Land near Ujani Dam Solapur District using TIMESAT Software

Proposed a smart doorbell system using webservers for making life easy and secure. In the proposed model, we are using Raspberry Pi for managing different types of interfaces and communications. We have used HTTP (Hypertext Transfer Protocol) for communication purpose. We have also used different types of software, including device software, control services, data derives and core services making this model efficient and salable. The overall aim of this paper is to make appropriate use of the technology for the needy and coming generations to come. In the proposed paper Histogram of Oriented Gradients (HOG) algorithm is used for Face Recognition.

Smart Doorbell Using web server IoT And AI

n the modern era of technology and with increasing dependency on smart techniques like mobile phones, there is a requirement of solving daily life tasks in quick and easy ways. Smart technology is becoming the need of the hour to take control over the different tasks at home and in industries. The system proposed is based on detection and recognition algorithms. The main function of the algorithms is to automatically detect the smell and generate a message to the user that the food is spoiled. The key feature of computer vision is Arduino for reasons like marketability &amp; law-abiding apps. Secondly, after a lot of research on the accessibility of realistic technologies, this area of research finds an important place among different types of researchers and scientists like computers, food &amp; different organizations. The microcontroller panel can perform functions that include interpreting inputs and outputs and making the sensor activated. Generally, food is stored in the refrigerator which lowers the bacteria rate of production. Certain items which are perishable or not used for long-term storage are to be detected and informed to the user. This project is based to solve the food spoilage through sensors by continuously sensing the signals from the food and sending the alert message to the registered mobile phone.

International Journal of Scientific Research and Engineering Trends

Interactive and Smart Refrigerator using IOT

The global seasonal change and continued rapid growth have maximized the need to assess the urban dwellers’ depend on vegetation for their lives, and also in the urban ecosystem resources. The conventional outcomes devoted to cropland mapping, with the help of high-quality remote sensing data’s. This paper is to investigate and develop a new methodology that pertains to time series analysis for classifying the type of vegetation in a farm area of Ujani Dam located in Solapur District, Maharastra. The proposed model develops a novel adaptive time-weighted dynamic time warping (ATWDTW) for the time series analysis using the satellite images. The gathered satellite images from the farm are processed initially and subjected to analysis by ATWDTW. The TWDTW concept is optimally tuned by the new hybrid meta-heuristic algorithm termed moth flame-based bird swarm optimization (MF-BSA) for enhancing the classification performance. Regarding the false omission rate of the proposed MF-BSA-ATWDTW model attains 5.56% and 29.9% lower than SVM and K-means respectively. From the analysis, it is possible to get a deep insight into the vegetation to be done in each year, and the comparative analysis proves that the proposed model is further adaptable for experimental use in relating and explaining environmental and ecological time-series information.

The ComputerJournal

Development of Adaptive Time-Weighted Dynamic Time Warping for Time Series Vegetation Classification Using Satellite Images in Solapur District

Land cover data is very significant for designing the earth system, managing the natural resources, and also for performing conservation planning. Time-series data are captured with their dynamic vegetation behavior using remote sensing technology, which is broadly utilized in land cover mapping. Most of the Vegetation Index (VI) such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) comprises commonly employed features that are obtained from the time-series spectral data. But, these VIs are not validated as the optimal techniques for generating the temporal profiles. Recent researches highly depend on optical satellite imagery for performing these above-mentioned tasks. Dynamic Time Warping (DTW) is said to be an effective optimal solution for solving the existing challenges, especially the improved version of DTW named Time-Weighted Dynamic Time Warping (TWDTW) is used for time-series analysis regarding the time-series vegetation classification. Yet, the TWDTW efficiency is not shown with other comparative machine learning approaches owing to the classification of vegetation type in the mountain areas. The major goal of this paper is to research and create a novel approach for distinguishing the kind of vegetation in a farm region near Ujani Dam in Solapur District, Maharashtra using time-series analysis. For time-series analysis employing satellite images, the suggested model offers a unique Adaptive Time-Weighted Dynamic Time Warping (ATWDTW). The farm's satellite images are first pre-processed before being sent to ATWDTW for examination. The TWDTW idea is optimized for classification performance using a new hybrid metaheuristic technique named Adaptive Coyote Crow Search Optimization (ACCSO). From the experimental results, the performance of the suggested ACCSO-ATWDTW correspondingly provides superior performance to the traditional approaches, where the designed model using ACCSO-ATWDTW provides 7.2%, 5.2%, 9.9%, 4.55%, and 2.33% higher MCC than the MFO-ATWDTW, BSA-ATWDTW, MF-BSA-ATWDTW, CSA-ATWDTW, and COA-ATWDTW at the maximum iteration of 200. This proved the robustness and less sensitivity to training samples of the TWDTW method when applied to mountain vegetation-type classifications. © 2024 World Scientific Publishing Company.

International Journal of Image and Graphics

Time-Variant Satellite Vegetation Classification Enabled by Hybrid Metaheuristic-Based Adaptive Time-Weighted Dynamic Time Warping

Accidents and road crashes occurring due to dissociation of driver from his consciousness is one of the hot topics researchers have been contemplating to curtail, from the past few years. This dissociation wanes the driver's ability to concentrate on the road while driving and promotes occurrence of mishaps. There has been a challenge to non-intrusively detect drowsiness and fatigue-level of drivers in real-time environments and concomitantly make it computationally faster and less complex. The paper proposes a faster real-time driver inactiveness detection system capable of alerting drivers of their drowsiness by inspecting and scrutinizing the state of eyes and mouth and follows a non-obtrusive approach. The system utilizes eyes to monitor the drooping nature of eyes and examines the mouth's current state to detect any yawning activity performed. Initially, it uses Haar transform to extract facial features and strong classifiers for detecting faces by incorporating Adaboost algorithm. Next, shape predictors are used to locate the key points for obtaining position of mouth and eyes within the face area. States of the mouth and eyes are continuously monitored with the help of the metrics viz. Eye Aspect Ratio (EAR) and Mouth Yawn Width respectively and are used to alert the driver through voice-enabled commands followed by a warning alarm sound to trigger them thereby aiding in regaining their consciousness back and focusing on their driving. The algorithm used in this system is tested under different lighting conditions and against different possible situations and accurate results in terms of successful detection rates are obtained. © 2022 IEEE.