One of the most typical malignancies of the liver is called hepatocellular carcinoma. This is a very dangerous illness that can even be fatal. Surgical removal of the tumor or a liver transplant may be effective treatments for hepatocellular carcinoma. In this article, we cover the topic of Hepatocellular carcinoma (HCC) liver cancer prediction models. We have suggested a simple method for predicting HCC liver cancer from a freely available dataset. In order to enhance the quality of the dataset, we are applying several pre-processing methods. We are using the Statistical model which is Logistic Regression for the classification, to predict the HCC Liver cancer disease at the early stage.

Keywords : Liver, Neural Network, Cancer , Machine Learning , HCC, DNA

Author : Kuppireddy Krishna Reddy

Title : Logistic Regression based Hepatocellular Carcinoma Liver Cancer Detection

Volume/Issue : 2025;2(1)

Page No : 1-4

Recent developments in complex machine learning models have drastically increased the accuracy of electricity price forecasting. In this paper, a combined model merging the RF and LSTM algorithms is given for improving price forecasting. The stated model expands the ability of the RF algorithm to find advanced interactions between features and the ability of the LSTM algorithm to identify temporal dependencies in time series data. The dataset set is of variables like demand, temperature, sunlight, and rainfall. Min-max scaling is applied to the preprocessing, along with the sliding window technique. Output describe that the combined hybrid model has improved accuracy than individual models with higher precision and recall values. Specifically, the hybrid model achieved 95. 87% accuracy, a precision of 0. 88, a recall of 0. 91, and an RMSE of 0. 032. The standalone Random Forest model was able to reach an accuracy of 93. 4%. The LSTM model achieved an accuracy of 94.1%. Further, hybrid model further improved the performance results in terms of precision, recall, and RMSE. Hence, this shows that the combined model is better suited for the task of forecasting electricity prices, which makes the hybrid model capable of delivering efficient real-time predictions required to make decisions in the energy markets. The results of the output are such that it indicates hybrid models that integrate RF and LSTM could deliver more dependence and practicality insights.

Keywords : Electricity Price Forecasting, Combined Model, Random Forest, LSTM (Long Short-Term Memory), Time-Series Forecasting, Machine Learning

Author : Kotamsetty Geethika Devi 1, Bandi Rajeswara Reddy 2, Galeti Mohammad Hussain 3¸ Gownivalla Siddartha 4

Title : Predictive Analytics for Electricity Markets Using a Hybrid Machine Learning Approach

Volume/Issue : 2025;2(1)

Page No : 5 - 10

The effectiveness of Cognitive Behavioural Therapy (CBT) for overcoming the irrational thought patterns that give rise to mental disorders is a known fact but pinpointing the cognitive pathways accurately for personalized treatment is the key. The advent of social media has made it possible for people to express their negative emotions, in a way that they disclose their cognitive distortions, and in the case of severe forms, manifest suicidal tendencies. Nevertheless, the techniques developed to analyze these lines of cognitive pathway are lacking hence, psychotherapists will have to wait until the symptoms get worse before they are able to act on time and with the right tools and methods, online environments will become a reality where they are the first to intervene in the situation. Decision Tree and Random Forest are the primary Hierarchical Text Classification models. They help the system for separating inputs from the users into the different cognitive pathways and simultaneously for detecting the negative thought pattern with the aid of these models. To be more specific, for the extraction of negative sentiment and only of that kind, the system is built with BERT for sentiment analysis in social media data. This system goes beyond the available ones by covering not only the negative thoughts but also predicting some subcategories within the negative and other physical as well as mental health categories. This update allows the understanding of the problems and also gives a basis for early detection and treatment to the psychotherapists in the form of psychological and mental health issues intervention and therefore helps in the prediction of social-emotional patterns.

Keywords : Cognitive Behavioral Therapy (CBT), Data Augmentation, Sentiment Analysis, Acceptance and Commitment Therapy , Text Classification, Phobias

Author : Kondreddygari Archana 1 , Suram Indhra Sena Reddy 2 , Shaik Meethaigar Jameer Basha 3 ¸ Shaik Karishma 4

Title : Data Augmentation for Cognitive Behavioral Therapy : Harnessing ERNIE Language Models Through Artificial Intelligence

Volume/Issue : 2025;2(1)

Page No : 11 - 17

Electricity price prediction plays a crucial role in energy markets, where accurate forecasts can help stakeholders optimize decision-making, reduce operational costs, and enhance market efficiency. Traditional forecasting models often fall short when faced with the complex, nonlinear nature of electricity price fluctuations. This study proposes a hybrid deep learning model combining ALEXNET and LSTM for accurate electricity price prediction. ALEXNET, a convolutional neural network, is used for feature extraction from historical price data, while LSTM captures the temporal dependencies in the price fluctuations. The integration of these models allows for effective learning of both spatial and sequential patterns, improving forecasting accuracy. Experimental results show that the hybrid approach outperforms traditional and standalone LSTM models, offering a promising solution for electricity price prediction. This method provides a more robust framework for optimizing energy market strategies and enhancing forecasting reliability. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. The proposed model applies to analysis on exact minimum-maximum scaling and a time window to predict the electricity prices for the upcoming too. Based on analysis and computational analysis to simulate the results, it gives far better than the traditional way for an exact accuracy rating of 97.08 calculations with comparison to earlier RNN and ANN calculations with accuracies of 96.64 % and 96.63% respectively. This research work is useful for smart Cities and Smart Villages too operational modes.

Keywords : Electricity Price Forecasting, Combined Model, Random Forest, LSTM , Time-Series Forecasting, Machine Learning

Author : Kotamsetty Geethika Devi

Title : Smart Electricity Price Predection using a Deep Learning

Volume/Issue : 2025;2(1)

Page No : 18 - 22

In today’s world humans present their opinions in social media , every day a large amount of text data is dumped ,these user text is used to Train a CBT model to classify emotions. Cognitive Behavioural Therapy (CBT) is a widely applied, proof-based practice to address human mental health problems using structured interventions. CBT incorporates advanced AI and deep learning techniques to enrich the CBT process. For classifying user-generated text into specific emotional categories based on analysing user intent, the CBT Model has utilized NLU models like BERT, RoBERTa. It uses datasets from Kaggle , Reddit, for emotion detection. The CBT model now generate response using XLNet based on user intent and provide a positive response to lead a healthy life .while existing model only classify where the user input text is depressed or Not Depressed, but proposed model not only classify user input but also provide suggestions to make users mental health stable. This intervention helps in considering AI is more comfortable and provide insight suggestion compared to psychotherapists

Keywords : Cognitive Behavioral Therapy, Data Augmentation, Sentiment Analysis, Acceptance and Commitment Therapy , Text Classification, Phobias

Author : Kondreddygari Archana

Title : Cognitive Behavioral Therapy for Sentimental Analysis using Artificial Intelligence

Volume/Issue : 2025;2(1)

Page No : 23 - 30