Power Theft Identification Using Smart Grid Technology

Paper deals with identification of online theft that prevails on distribution line. Here used smart grid technology to identify theft online. Smart grid means an effective two-way communication between sending and receiving end. Here used microcontroller based system to detect power theft .Taking consideration grid & resident designing intelligent program which is main concept of paper. DOI: 10.17762/ijritcc2321-8169.15015

Enhanced Rice Crop Yield Prediction Through Fuzzy Logic Modeling

Predicting rice yield accurately plays a pivotal role in agricultural planning, resource allocation, and food security strategies. This paper proposes a comprehensive rice yield prediction model leveraging machine learning techniques, with a focus on Fuzzy Logic. The model integrates diverse datasets, including historical yield records, meteorological data, soil characteristics, and crop management practices. Through rigorous data preprocessing, feature engineering, and selection, relevant features are extracted to capture the complex relationships influencing rice yield. The machine learning model, utilizing Fuzzy Logic, is trained and validated to ensure robust performance and generalization capability. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared are employed to assess model accuracy. The proposed model provides accurate predictions of rice yield, empowering stakeholders with valuable insights for informed decision-making in agriculture. This research contributes to the advancement of predictive modeling techniques in agriculture, facilitating sustainable crop production and food security

Detail image Enhancement Survey

Image enhancement plays important role in the field of image processing. Many images suffer from poor contrast and noise. There is requirement of enhancing the contrast& removing noise to improve image quality. Image enhancement is the process of improving quality of image. Image enhancement produces the image which will give better result than original image. Detail image enhancement is introduced in the field of image processing to solve many problems like blurring, ringing, unnaturalness etc. Detail image enhancement algorithm first decompose source image into a base layer and detail layer via edge preserving smoothing algorithm and amplify detail layer to produce to detail enhanced image. Analysis of different methods of image enhancement is carried out. Existing image enhancement techniques have some drawbacks. The objective of this paper is to determine limitation of the existing image enhancement techniques

A Simulation Study on Warpage Analysis of Injection Moulded Plastic Part

A part to be injection molded is evaluated by simulation for warpage analysis. The plastic part is a supporting plate to be used in the oil filter and it’s made out of nylon material. The effect of various parameters from design to processing of plastic parts is considered and validated by simulation results. The research involved in this was designing mould, computer-aided engineering, simulation analysis, and determination of plastic part processing conditions.In this work PA66 (Grade name – Zytel 70G13HS1LNC010) material is used and the material contains 13 % of fiber. Fiber orientation is nothing but the distribution of plastic melt inside the cavity and it also plays important role in deciding the warpage of part.The effect of process parameters on part warpage is investigated from various aspects in comparison with the conventional runner system. Hot runner mould system with innovative cooling channel designs is good results-driven. Results of simulations reveal that elevated mould temperature reduces the unwanted freezing time during the injection phase and thus improves mouldability and enhances part quality. Under similar mould temperature conditions, the effect of process parameters on warpage decreases according to the following order, packing time, packing pressure, melt temperature, injection pressure, and cooling time respectively

Lightweight MobileNet Model for Image Tempering Detection

In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images

Landsliding Pre-Warning System

In this project we have to study of landslide, it occurs naturally we can’t stop natural cause but we can alert the people. Due to landslide there will losses of human life and properties. This project present landslide alert system by using wireless sensors that transmitted by zigbee module from this we can alert the people. In this we used three sensors accelerometer sensor, water level sensor, temperature sensor. Accelerometer sensor is used to measure the slop of angle if there is any movement in landslide and we used water level sensor to collect the depth of water in land. Temperature sensor is used to check the change in temperature. This data is given to microcontroller it is used to read the measurement and display on LCD. GPS is used to give latitude and longitude all reading is given to transmitter zigbee. This information is transmitting to receiver zigbee which is display on LCD and buzzer will activate due to this we can alert people and save human life and properties. This is real time project to save the human life

A Novel Hybrid AI Federated ML/DL Models for Classification of Soil Components

The soil is the most fundamental component for the survival of any living thing that can be found on this planet. A little less than 41 percent of Indians are employed in agriculture, which accounts for approximately 19 percent of the country's gross domestic product. As is the case in every other industry, researchers and scientists in this one are exerting a lot of effort to enhance agricultural practices by utilising cutting-edge methods such as machine learning, artificial intelligence, big data, and so on. The findings of the study described in this paper are predicated on the assumption that the method of machine learning results in an improvement in the accuracy of the prediction of soil chemical characteristics. The correlations that were discovered as a result of this research are essential for comprehending the comprehensive approach to predicting the soil attributes using ML/DL models. A number of findings from previous study have been reported and analysed. A state of the art machine learning algorithm, including Logistic Regression, KNN, Support Vector Machine and Random Forest are implemented and compared. Additionally, the innovative Deep Learning Hybrid CNN-RF and VGG-RNN Model for Categorization of Soil Properties is also implemented along with CNN. An investigation into the significance of the selected category for nutritional categorization revealed that a multi-component technique provided the most accurate predictions. Both the CNN-RF and VGG-RNN models that were proposed were successful in classifying the soil with average accuracies of 95.8% and 97.9%, respectively, in the test procedures. A study was carried out in which the CNN-RF model, the VGG-RNN model, and five other machine learning and deep learning models were compared. The suggested VGG-RNN model achieved superior accuracy of classification and real-time durability, respectively