International Journal of Computer Communication and Informatics

Weed Identification Using Convolution Neural Networks

Murali Krishna B — 2023-12-13

Deep learning is the core component of the machine learning field which employs knowledge representation for learning. Learning can be supervised or unsupervised. More deep learning techniques can be used which will contain deep belief, deep neural, recurrent neural networks in it which will be used in many fields. The most commonly used applications in deep learning are vision, audio, video, language processing, social media, medical, gaming and there are so many other programs where this deep learning has already produced very perfect results when compared to other cases and in a very little number of cases with superior to experts i.e. humans. Techno Agriculture is the domain where the farmers will get benefited from these latest improvements in the expert system. One of main objectives is that in order to remove weeds or unwanted plants by reduction in the usage of herbicides and to decrease the pollution in both crop and water. One of the Neural Networks i.e. CNN uses a flexible layer with the function of a ReLU to extract image elements and then uses a high-resolution and fully integrated RELU layer to separate weeds from the plant. The image which was processed previously is used on the convolution neural network which in return gives an image from the Region of Interest (ROI) from where it will extract the image and remove the certain aspects of the image in the training phase, after the training a splitting operation will be performed and the weeds are therefore classified by using the deep learning technique. In this scenario we trained 100 images in order to increase the accuracy of the model.

Unveiling Future Trends for Predicting Online Smart Market Stock Prices using Ensemble Neural Network

Deepa N — 2023-12-13

Predicting stock prices in the online smart market is a complex task, and leveraging advanced data mining techniques has become essential for accurate forecasting. This study proposes a novel approach utilizing an ensemble neural network combined with swarm optimization for enhanced predictive accuracy. The ensemble neural network, a robust machine learning approach, is adept at capturing complex patterns in stock market data. Concurrently, swarm optimization further refines the model's predictive capabilities, optimizing parameters for superior performance. By incorporating these techniques, the study unveils future trends in predicting online smart market stock prices, providing investors and traders with invaluable insights for informed decision-making. Existing algorithms are limited. The ensemble neural network integrates diverse models to capture intricate patterns in financial data, while swarm optimization refines the model parameters for optimal performance. The experimental results showcase an impressive accuracy of 92.5%, highlighting the efficacy of the proposed methodology. This research not only contributes to the field of stock price prediction but also provides valuable insights into future trends in the online smart market.

Dual Axis Solar Tracking of Solar Radiation for Agriculture usage

Monika Gupta — 2023-12-18

Energy is one of the important parts of our life. As there is decline in fossil fuels and increasing demand for energy an alternate energy source is required which is renewable energy source like solar, wind etc. So, we use solar panels which trap the energy from the sun and produce electricity, and this energy is used for agriculture purpose like to run water pumps and to meet other energy requirements in agriculture. Due to rotation of earth the stationery solar panel will receive energy only for smaller duration so to overcome this we use dual axis tracking system which rotates solar panel according to direction of sun and helps in producing more solar energy. Agriculture is one of the major contributing sectors to the economy of a country and it requires automation and advanced technology so that it helps farmers in producing more yield and better crops. So, in agriculture continuous monitoring of soil and water level is required so we can automate this which helps the farmers where the device continuously monitors and depending upon the moisture level of the soil the water pumps get on automatically and we can use this for different crops and set threshold depending upon the crop type. And we can also integrate this idea with IOT technology for improvements. By this we create sustainable energy indirectly producing sustainable environment.

Deceptive Content Analysis using Deep Learning

Hritik Gupta — 2023-12-21

Fake news is the deliberate spread of false or misleading information through traditional and social media for political or financial gain. The impact of fake news can be significant, causing harm to individuals and organizations and undermining trust in legitimate news sources. Detecting fake news is crucial to promote a well-informed society and protect against the harmful effects. Tools such as machine learning and natural language processing are being developed to help identify fake news automatically. Necessity of fake news detection is very important to maintain a trustworthy and responsible media environment. We have used Word2Vec model for word vectorization and represents words in a multi- dimensional space based on their semantic and syntactic relationships. The use of the LSTM with 256 units allows our model to capture the sequential nature of the data and make predictions based on past information. The proposed model uses Word2Vec and LSTM models to provide a powerful approach to fake news detection, combining the ability to capture the complexity of language and the sequential nature of the data. The model has the potential to accurately detect fake news and promote a well-informed society. The accuracy achieved by building the model was 97%.

Harnessing Effectiveness of ResNet-50 and EfficientNet for Few-Shot Learning

Santoshi Vajrangi — 2023-12-24

Inspired by the concept of human intelligence- learning and expanded upon with several examples – several- step learning focused on computers that can classify images in a comparable way. This article covers the interesting field of sparse learning, focusing on comparing its implementation using two popular deep learning networks: ResNet and EfficientNet. Little learning has the potential to be effective on tasks where obtaining large data sets is expensive or impossible. This allows machines to mimic humans’ ability to learn and expand from small samples, thus opening possibilities in the field of several types of diagnostics, personalized recommendations, systems, and robotics. Our main goal is to measure and compare the accuracy achieved by these models when learning on limited datasets and to show that EfficientNet achieves better accuracy when it requires fewer parameters and computational resources compared to ResNet-We considered VGG-flowers dataset for comparison. Our results show that Narrow EfficientNet outperforms ResNet-50 in terms of overall accuracy (85.20% vs. 84.30%), precision (85.60% vs. 85.40%), recall (85.30% vs. 84.50%) and F1 acquisition (85.45% vs. 84.95%). This suggests that EfficientNet’s emphasis on computational efficiency and parallelism may provide a slight advantage on limited data.