The current study intention is discovering Deep Intelligent Networks incorporating Reinforcement Learning hooked as Cloud-Based ITS sustaining adaptive, scalable as well as concurrent road traffic flow supervision. Customary means of transportation exhibits restrictions in operational executive resolution, scalability or immediate action. Hence the current study recommends diverse agenda which is capable of calculating, improving as well as familiarizing concurrent vehicle movement. Deep learning along with Deep Intelligent Network exercise massive, multidimensional evidence through countless smart image capturing sensors, global positioning systems and smart location trackers. Feature abstraction as well as pattern configuration result in mechanically tag files that may be vehicle compactness as well as rotation where manual codes fails to control these issues. Road traffic indicator device which may be considered as agent correlates with road traffic network considered as environment so that it can send altering signals or redirecting direction to implement action that may exploit actions to obtain persistent reward like subsiding normal waiting phase or decreasing overcrowding. Scalability and supervising evidences with the help of thorough conservational array can be established easily with the help of cloud computing sources. The cloud computing infrastructure will enable scalable data processing and distributed learning, supporting large-scale deployments in smart city environments. The current study anticipates considerably shrinking jamming of vehicles, enhancing safety and rousing township drive that can be easily maintainable. Datasets that can be replicated as well as applicable are considered in model preparation along with performance appraisal.

Keywords : Deep Intelligent Networks, Agents, Reinforcement Learning , Cloud Computing, Deep Learning.

Authors : D Venkata Siva Reddy , K Satyanarayana Reddy

Title : Deep Intelligent Network and Reinforcement Learning for Cloud-Based Intelligent Transportation Systems

Volume/Issue : 2025;2(1 (January - March))

Page No : 53 - 56

The IoT-based Electrical Vehicle Battery and Fire Protection Management System work focuses on developing a comprehensive safety and monitoring system for Electric Vehicle (EV) batteries. Utilizing a microcontroller, the system integrates various sensors and continuously monitor the health and performance of the EV battery. Key components include a voltage sensor for tracking battery voltage levels, and a DS18B20 temperature sensor to monitor the battery temperature. A Battery Management System (BMS) is a critical component in electric vehicles (EVs) and other battery powered systems. It monitors and controls the operation of the battery pack, ensuring its optimal performance, safety, and longevity. Cooling equipment is also integrated to help cool the battery when temperatures rise above safe levels. A buzzer provides an audible alert for the user in case of any critical conditions. Using latest wireless technologies, sensor data will be sent to the IOT platforms like ThingSpeak application. The system displays real-time data on an LCD screen, offering an interface for monitoring battery conditions. By ensuring timely detection of critical issues such as overheating, this system enhances the safety and reliability of electric vehicle batteries, preventing potential fire hazards and maximizing the battery’s lifespan.

Keywords : Battery Management System, IOT, Raspberry Pi, Temperature sensor, Voltage sensor.

Authors : P Gangadhara Reddy , T Ramashri , A Subramanyam

Title : IoT Based Electrical Vehicle Battery Management System with Charge Monitoring and Fire Protection

Volume/Issue : 2025;2(1 (January - March))

Page No : 47 - 52

STHE play a crucial role in facilitating efficient heat transfer between fluids in diverse industrial applications. To optimize performance, these heat exchangers often incorporate inclined baffles in their design. This research delves into the impact of inclined baffles on fluid flow properties and heat transmission within a STHE Strategically positioned within the shell side, the inclined baffles aim to enhance turbulence and disrupt laminar flow, thereby improving heat transfer rates. Through a combination of experimental studies and computational simulations, the performance of the heat exchanger with inclined baffles is compared to that without. The findings reveal a substantial increase in heat transfer efficiency with the incorporation of inclined baffles. This outcome suggests that the addition of inclined baffles presents a viable strategy for boosting thermal performance in various industrial processes. The deliberate placement of these baffles proves to be an effective means of augmenting heat transfer in STHE making them a valuable enhancement in the realm of industrial heat exchange systems.

Keywords : STHE, inclined baffles, turbulence enhancement, heat transfer efficiency, industrial heat exchangers.

Authors : Akshay C , Mejo M Francis , S Christopher Ezhil Singh , P Sridharan , T Mary Little Flower , S Jerril Gilda

Title : A Study on the Green Synthesis of Silver Nanoparticles and Their Applications

Volume/Issue : 2025;2(1 (January - March))

Page No : 43 - 46

The many aspects of robotic-assisted minimally invasive surgery (RAMIS) and associated technologies are examined in this thorough overview of the literature. The works that are emphasized explore the developments and difficulties in this subject, which has a wide range of applications from neurovascular treatments to orthopedic surgery. Examining topics such as recording femur motions, remote sensing in agriculture, and the effect of direct-to-consumer advertising on patient outcomes in robotic coronary artery bypass grafting, the changing environment of RAMIS is discussed. Technology is explored, with a focus on how robotic surgery is becoming more and more common, with a focus on how it is spreading to various operations despite the scant proof of its clinical benefits. The advantages and disadvantages of RAMIS are discussed, including lower hospital stays and scarring but also higher expenses and more complicated technological requirements. In addition, methods for validating multi-robot surgery platforms are examined, as is the possibility of using soft surgical robots to improve minimally invasive surgery. Specific applications, including esophagectomy, semantic segmentation on surgical equipment, and RAMIS utilization in head and neck surgery, are also covered in the paper. Present topics include the difficulties in delivering haptic feedback, insights into intelligent surgical robots, and the use of data and artificial intelligence to improve surgical performance. Topics covered include the viability of virtual surgical mentorship and the design and assessment of user interfaces for teleoperated surgical robotic systems regarding the outcomes of colorectal cancer surgery using robotics and laparoscopy. A thorough summary of the state of RAMIS today, as well as its obstacles and prospects, is given in this synthesis, which will be of great use to scholars, medical professionals, and other interested parties in the rapidly changing field of MIS.

Keywords : Robotic-assisted minimally invasive surgery, surgical robotics

Authors : C Abhinav , Midhun Mukundan M K , S Christopher Ezhil Singh , P. Sridharan , T Mary Little Flower , S Jerril Gilda

Title : Advancements in Tele Operated Robotics for Minimally Invasive Surgical Procedure

Volume/Issue : 2025;2(1 (January - March))

Page No : 38 - 42

Human sources are those who make the group of workers of a company. It's also recognized via manpower, skills, labour, employees, etc. Human Resource department of a business enterprise performs human useful resource control. It entails various elements of the employment consisting of compliance with labour regulation and employment standards, management of worker benefits, and various other sports related to recruitment and choice of the employee. Worker retention is regarding the efforts with the aid of which employers try and retain the personnel in their team of workers. Retention turns into the strategies in place of the final results. Preserving the worker for long duration of time is known as retention. Retention strategies of the organization need to have the capacity to attract and hold their staff. Organizational effectiveness refers to a company's ability to achieve the goals it sets out to achieve. It's far the performance of the organization, group or an organization to fulfil its goal. Six Sigma is a methodology that makes a specialty of improving the overall efficiency of a business process.

Keywords : Employee Retention, Reasons of Employee Turnover, Strategies of Employee Retention.

Authors : S Patan Naziya Khanam

Title : A Conceptual Study on Employee Retention Strategies in enhancing Organizational Effectiveness

Volume/Issue : 2025;2(1 (January - March))

Page No : 34 - 37

The ongoing digital transformation is profoundly reshaping the dynamics of the modern workforce, particularly in the realm of leadership and management. As organizations increasingly adopt advanced technologies, such as artificial intelligence, automation, and data analytics, leaders and managers are faced with the challenge of adapting their styles to ensure the success of these changes. This study explores the impact of digital transformation on leadership and management approaches, emphasizing the need for agility, innovation, and emotional intelligence. It examines how leaders are shifting from traditional hierarchical models to more collaborative, inclusive, and flexible styles in response to the demands of a digitally enabled workforce. Additionally, the research highlights the crucial role of digital literacy and strategic foresight in leadership development, ensuring that leaders can navigate technological disruptions and foster a culture of continuous learning and adaptation. The findings suggest that successful leaders in the digital age are those who can balance technological expertise with human-centered leadership qualities, creating environments that promote creativity, engagement, and overall workforce performance.

Keywords : Digital Transformation, Leadership Styles, Management Styles, Workforce Dynamics.

Authors : Rajani N

Title : The Impact of Digital Transformation on Leadership and Management Styles

Volume/Issue : 2025;2(1 (January - March))

Page No : 30 -33

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

Authors : Kondreddygari Archana

Title : Cognitive Behavioral Therapy for Sentimental Analysis using Artificial Intelligence

Volume/Issue : 2025;2(1 (January - March))

Page No : 23 - 30

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

Authors : Kotamsetty Geethika Devi

Title : Smart Electricity Price Prediction using a Deep Learning

Volume/Issue : 2025;2(1 (January - March))

Page No : 18 - 22

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

Authors : Kondreddygari Archana , Suram Indhra Sena Reddy , Shaik Meethaigar Jameer Basha ¸ Shaik Karishma

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

Volume/Issue : 2025;2(1 (January - March))

Page No : 11 - 17

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

Authors : Kotamsetty Geethika Devi , Bandi Rajeswara Reddy , Galeti Mohammad Hussain ¸ Gownivalla Siddartha

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

Volume/Issue : 2025;2(1 (January - March))

Page No : 5 - 10

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

Authors : Kuppireddy Krishna Reddy

Title : Logistic Regression based Hepatocellular Carcinoma Liver Cancer Detection

Volume/Issue : 2025;2(1 (January - March))

Page No : 1-4