Our project presents an innovative approach to address the crucial issue of plant disease identification through the utilization of deep learning techniques. Agricultural productivity is significantly affected by plant diseases, leading to economic losses and food security concerns. In this research, a comprehensive dataset comprising images of healthy and diseased plants is collected and pre-processed for training deep convolutional neural networks (CNNs). The proposed system harnesses the power of deep learning to automatically learn intricate patterns and features from plant images, enabling accurate classification between healthy and diseased states. The trained model is evaluated on an independent dataset to assess its classification performance. Various deep learning architectures, such as VGG, ResNet, and Inception, are experimented with to identify the most suitable architecture for achieving high accuracy and robustness. To enhance the model's generalization capability, data augmentation techniques are employed during training. Transfer learning is also explored, allowing the pre-trained models to be fine-tuned for the specific task of plant disease classification. The performance metrics, including accuracy, precision, recall, and F1-score, are thoroughly evaluated to quantify the model's effectiveness. The proposed deep learning-based plant disease classification system holds great promise for real-world agricultural applications. Its automated and accurate nature has the potential to revolutionize plant disease management by enabling early detection and timely intervention. As a result, this research contributes to the advancement of precision agriculture practices, helping to mitigate the adverse impacts of plant diseases and promote sustainable agricultural production.

Keywords : Recycled Deep Learning Techniques, CNN, Deep Learning Architectures (VGG, ResNet, Inception.

Authors : D.siva sankar Reddy

Title : Automated Plant Disease Detection and Classification using Deep Neural Networks

Volume/Issue : 2024;6(6 (November - December))

Page No : 38 - 42

Due to the continuous demand for concrete to meet various requirements, extensive and wide spread research work is being carried out in the area of concrete technology. Construction activities generate a significant amount of concrete waste, posing environmental and economic challenges. Usage of recycled coarse aggregate (RCA) in concrete production offers a sustainable solution by reducing reliance on virgin resources and minimizing landfill waste. This project aims at assessing the strength characteristics of concrete incorporating 40% recycled aggregate while employing diverse curing techniques. The study has also been done to investigate the influence of different curing methods on the compressive and tensile strength of the concrete mix containing recycled aggregates. The study reveals that this kind of mixture with 40% recycled aggregate cured using water immersion method can successfully be used for construction of low-rise buildings and pavements. Thus, this project holds significance in sustainable construction practices.

Keywords : Recycled Coarse Aggregate, Concrete, Compressive and Tensile Strength.

Authors : Mrs.K.Rehana

Title : Sustainable Strength Development of Recycled Aggregate Concrete under Varied Curing Conditions

Volume/Issue : 2024;6(6 (November - December))

Page No : 32 - 37

The software defect prediction technique yields result that development teams may examine and further contribute to industrial results. It finds all the problematic code portions, helps software developers uncover bugs, and helps them design their testing methods with the help of the model prediction. It is essential to know what percentage of categories yield the accurate forecast for early detection. Moreover, software-defected data sets are supported and at least partially recognized due to their huge dimension. Random forests (RF) and Artificial Neural Networks (ANN) are the machine learning techniques utilized in this research. The forecast for defects is created using historical data. The outcomes showed that the artificial neural network classifier performed better than the random forest classifier.

Keywords : Software Errors, Machine Learning, ANN, Random Forests.

Authors : B.J.Priyanka

Title : A Comparative Study of Machine Learning Techniques for Software Defect Forecasting

Volume/Issue : 2024;6(6 (November - December))

Page No : 26 - 31

The main goal of this work is to develop and implement a 4-bit binary adder based on Quantum-Dot Cellular Automata (QCA). An integral part of digital computer systems, the adder is necessary to execute arithmetic operations on binary values. In this project, several adder circuit components—such as inverters, half adders, full adders, and a 4-bit binary adder made up of several full adders—are created utilising QCA technology. Several methods, including cascading several adder modules and using logic gates and transmission gates, are used in the implementation. The significance of this study is in designing high-performance and compact adder circuits by utilising the special qualities of QCA, such as ultra-fast computation, energy efficiency, and scalability. The goal of the research is to show the benefits and viability of using QCA-based computing for arithmetic operations, clearing the path for the creation of strong and effective computer systems. Key findings from earlier studies in QCA-based computing are compiled through an extensive literature review, offering a strong basis for the endeavour. The literature review includes contemporary developments in adder design and optimisation utilising QCA in addition to classic publications on QCA technology. The project involves using QCA-based simulation tools to create and simulate several adder components. Through simulation tests, the adder circuits' functionality and performance are assessed, offering important insights into how well they work. Additionally, a thorough examination of the suggested adder designs is provided, emphasising their benefits and their uses in digital computing systems.

Keywords : QCA, Full adder, 4 bit binary adder, half adder.

Authors : cManoj kumar

Title : Quantum-Dot Cellular Automata bases 4-Bit Binary Adder Design

Volume/Issue : 2024;6(6 (November - December))

Page No : 18 - 25

In recent technology Visible Light Communication (VLC) is utilizes visible light to transmit the data. While it's commonly used for indoor lighting, it can also be harnessed to transmit audio, text, and even voice signals. it's worth noting that VLC faces some challenges compared to traditional wireless communication technologies. For instance, VLC requires a direct line of sight between the transmitter and receiver, making it less suitable for long-range communication or scenarios with obstacles obstructing the light path, because it has few advantages over other standard wireless transmissions. The advantages of VLC are low power consumption, high data rate, more security and can avoid interference occurs. Under various constraints, including light intensity, output quality, and distance, the transmitter and receiver system will combine the audio, text, and speech transmission utilizing Li-Fi and validate its performance. Li-Fi employs standard light for transmissions at 10 Kbit/s or LED for up to 500 Mbit/s of connectivity over short distances. This paper describes a system that can provide data over a range of distances through experimentation. We are also implementing the transmission of Audio, Voice and Text Using Visible Light Communication for indoor location-based services.

Keywords : Audio transfer; Li-Fi, voice transfer, text transfer, visible light communication and Arduino uno

Authors : K.V. Vara prasad

Title : Transmission of Audio, Voice and Text using Visible Light Communication

Volume/Issue : 2024;6(6 (November - December))

Page No : 14 - 17

Chatbot for educational institutions that makes use of natural language processing (NLP) and neural network techniques. The chatbot is a creative way for educational institutions to improve support and communication. Through the utilization of neural networks and natural language processing, the chatbot enables smooth communication between staff, faculty, and students. It provides individualized support for questions about academic resources, scheduling, course details, and administrative processes. Because of its neural network architecture, the chatbot can comprehend and provide responses that are similar to those of a human, which enhances user engagement and experience. The chatbot learns continuously, improving its ability to understand context, tailor its responses, and adjust to changing linguistic patterns. Sentiment analysis and intent recognition are two NLP approaches that enable the chatbot to quickly and accurately evaluate user requests and deliver pertinent information.

Keywords : Machine Learning, NLP, Data Collection, AI, Data Science, Anaconda, Navigator, Django, CSS.

Authors : P.Chandra sekhar

Title : Educational Institute based Chatbot using Natural Language Processing

Volume/Issue : 2024;6(6 (November - December))

Page No : 11 - 13

This paper presents the design, optimization, and analysis of a dual?band F?shaped microstrip antenna incorporating a Defected Ground Structure (DGS) for improved bandwidth and gain. The proposed antenna operates efficiently at 2.375?GHz and 5.325?GHz, offering reflection coefficients of ?30.6?dB and ?41.85?dB, respectively. The integration of a partial ground and optimized stubs enhances impedance bandwidth and return loss characteristics. Simulation results show impedance bandwidths of 270?MHz and 1.12?GHz, with respective gains of 2.3?dBi and 3.5?dBi, indicating suitability for WLAN and Wi?Fi applications.

Keywords : Microstrip patch, DGS, WLAN and Wi-Fi.

Authors : Jada Nageswara Rao

Title : Design and Performance Analysis of a Dual Band F-Shaped Microstrip Antenna Using Defected Ground Structure

Volume/Issue : 2024;6(6 (November - December))

Page No : 7 - 10

Underwater images find application in various fields, like marine research, inspection of aquatic habitat, underwater surveillance, identification of minerals, and more. However, underwater shots are affected a lot during the acquisition process due to the absorption and scattering of light. As depth increases, longer wavelengths get absorbed by water; therefore, the images appear predominantly bluish-green, and red gets absorbed due to higher wavelength. These phenomenon’s result in significant degradation of images due to which images have low contrast, colour distortion, and low visibility. Hence, underwater images need enhancement to improve the quality of images to be used for various applications while preserving the valuable information contained in them.

Keywords : Histogram Equalization Technique, White Balance Technique, DCT Technique.

Authors : Jada Nageswara Rao

Title : Underwater Image Enchancement using CNN based Learning Methods

Volume/Issue : 2024;6(6 (November - December))

Page No : 1 - 6