Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques
Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Microwave Link Failure; Microwave radio; Quality of Service
Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers. 2025 IEEE.
Okewumi, Peter Olufeyi; Ojo, Olayinka Anthony; Iwendi, Celestine; Sharma, Vandana; Uwah, Salome Enoshi; Aboutorabi, Negin
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICRITO66076.2025.11241758 https://www.scopus.com/pages/publications/105029853032?origin=resultslist
Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Comparative Analysis of Machine Learning Models for Uterine Cancer Prediction Using Clinical and Genomic Data
Clinical Data; Genomic Data; Machine Learning; Medical AI; Prediction; Random Forest; Uterine Cancer; XGBoost
Uterine cancer prediction accuracy is important in clinical decision-making because it improves the overall chances of patient recovery. Several machine learning models, such as Decision Tree, Random Forest, XGBoost Regressor, and Support Vector Regressor, were explored to determine which is more effective in predicting uterine cancer. Attributes such as mutation counts, diagnosis age, and MSI score, were used for the analysis. The different models were tested using the standard performance metrics such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 Score. Random Forest showed the highest predictive performance with an R2 score of 0.655, followed by XGBoost regressor, which was relatively close to the R2 score of Random Forest. Support Vector Regressor performed very poorly as the R2 score was negative, implying that the model is not suitable for such prediction. Ensemble-based models, which include Random Forest and XGBoost Regressor, have proven to be more effective in handling medical prediction tasks, and this is because of their robustness and their ability when it comes to handle overfitting. Though model generalizability was affected due to small data size and the absence of hyperparameter tuning. The future work will focus on expanding the dataset, implementing hyperparameter tuning, integrating deep learning, and leveraging explainable AI (XAI). The research has provided valuable insight for clinicians who wish to use machine learning for uterine cancer prognosis. 2025 IEEE.
Nwigwe, Simon; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Uwah, Salome Enoshi; Nwibo, Ezekiel Gabriel
https://doi.org/10.1109/ICRITO66076.2025.11241682 https://www.scopus.com/pages/publications/105029800501?origin=resultslist
Nwigwe S., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Nwibo E.G., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Harnessing Behavioural Insights for Autism Spectrum Disorder Prediction via Machine Learning
autism spectrum disorder; machine learning; neural networks; random forest; stress management; support vector machines
This paper exploration has been done for predicting autism spectrum disorder or ASD while using certain machine learning classifiers. The multisource dataset used, covers behavioral, genetic, neuroimaging and sensor data. The classifiers used are: Random Forest, AdaBoost, Extra Trees, Logistic Regression, K Neighbors, and Bernoulli Naive Bayes. The results achieved are: The Bernoulli Naive Bayes (92.10%), Random Forest (91.53%) and Logistic Regression (91.34%). AdaBoost and Extra Trees performed well with accuracies 90.34% and 90.20%, respectively. K Neighbors Classifier had the lowest accurate outcome with 87.65%. This study explores improving ASD diagnosis, highlighting the effectiveness of various models and emphasizing the need for further research to address challenges such as model interpretability and data quality. 2025 IEEE.
Rajak, Akash; Kumar, Amit; Singh, Shweta; Vidushi; Verma, Ankit; Mishra, Siddheshwari Dutt
https://doi.org/10.1109/ICRITO66076.2025.11241561 https://www.scopus.com/pages/publications/105029842779?origin=resultslist
Rajak A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Kumar A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Singh S., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Vidushi, Christ (Deemed to be University), Bengaluru, India; Verma A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Mishra S.D., Mriirs, Faridabad, India
Detection and Sensing of Human Body Micro-Motions Using 24GHz mm-Waves: A Case Study
24 GHz mm-Wave; beam-forming; Doppler effect; human motion detection; IoT; micro-motion sensing; MIMO; signal attenuation; smart environments
The paper presents a 24 GHz millimeter-wave (mm-Wave) system for real-time human presence and distance detection, leveraging the 24 GHz band's balance of range, resolution, and power efficiency for applications in smart environments. The system uses Doppler-based reflections from signals to accurately detect presence and estimate distances with 12cm accuracy up to 5 meters with little needed infrastructure. The challenges presented by signal attenuation and multipath interference are addressed using beamforming and Massive MIMO with 5G-enabled IoT as the framework. The use of Ultra-Reliable Low Latency Communication and Massive-Machine-Type Communication allows a framework for rapid data processing and scalability. This system enables automated smart home lighting, healthcare occupancy detection, and security intrusion alert applications, with very high detection accuracies. The limits are reduced detection performance beyond a distance of 6 meters and interference from reflective surfaces. Future work includes investigating the 60 GHz bands, which will yield higher resolution in the detection, and using machine learning techniques to develop adaptive detection, as a scalable and cost effective method of real-time automation in a range of settings. 2025 IEEE.
Joy, Salna; Rajesh, Siddharth; Neethu, P.S.
https://doi.org/10.1109/ICRITO66076.2025.11241290 https://www.scopus.com/pages/publications/105029820572?origin=resultslist
Joy S., New Horizon College of Engineering, Department of Computer Science and Engineering, Bengaluru, India; Rajesh S., New Horizon College of Engineering, Department of Electronics and Communication Engineering, Bengaluru, India; Neethu P.S., School of Engineering and Technology, Christ University, Dept of Aiml and Data Science, Bengaluru, India
Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques
ARIMA; Artificial Intelligence; Energy Management; Machine Learning; Prophet; Renewable Energy; SARIMA
The exponential increase in campus energy consumption results from the rise in population density, leading to urbanisation and the use of higher energy-intensive devices within the environment. This study explored high-performance data analytics techniques to visualise energy consumption across buildings using datasets obtained from a load audit of the entire distribution network within the Federal University of Technology, Owerri (FUTO). Advanced time series models were used to predict and forecast the consumption patterns for a year. Visualisations for this research provided detailed insights into the energy profile across all the clusters, while the SARIMA, ARIMA, and Prophet models predicted the energy demands. The heatmap for the correlation matrix reveals a constant energy scale throughout the week (weekend average energy usage is at least 40% of the weekday). A comparative performance was done to analyse the scalability and predictive abilities of the individual models. Results from the study indicate that SARIMA has the lowest mean square error (4.4896) and the highest R2 score (0.8362). The study concludes that the adoption of machine learning models for energy forecasting and prediction is vital for modern-day energy management in the University. 2025 IEEE.
Nwibo, Ezekiel Gabriel; Iwendi, Celestine; Sharma, Vandana; Nwigwe, Simon; Ojo, Olayinka Anthony; Odirichukwu, Jacinta Chioma
https://doi.org/10.1109/ICRITO66076.2025.11241252 https://www.scopus.com/pages/publications/105029841206?origin=resultslist
Nwibo E.G., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Iwendi C., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Nwigwe S., Doctoral College, University of Greater Manchester, Department of Research, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Odirichukwu J.C., Federal University of Technology Owerri, Department of Computer Science, Nigeria
Deep Learning-Based Health Risk Prediction in Contact Sports Using Wearable Sensor Data
Athlete Health; Collapse risk Prediction; Contact Sports; LSTM-long short-term memory; Monitoring; PAMAP2; Wearable sensors
This study presents a deep learning-based approach to predicting physiological health risks in athletes engaged in contact sports using wearable sensor data. Motivated by the need to detect early warning signs of collapse or severe fatigue, this study employs a Long Short-Term Memory (LSTM) neural network to analyse multivariate time-series data. Key physiological signals, including heart rate, body temperature, and motion, were extracted from the PAMAP2 dataset to train and validate the model. The LSTM demonstrated strong predictive performance, achieving an accuracy of 98.3% in identifying potentially dangerous physiological states. In addition to its high classification accuracy, the model effectively captured temporal dependencies in the data, underscoring its suitability for health risk prediction in dynamic, high-intensity sports environments. This study highlights the potential of wearable data and LSTMbased analysis in supporting proactive athlete health management and injury prevention. 2025 IEEE.
Uwah, Salome Enoshi; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Okewumi, Peter Olufeyi; Nwigwe, Simon
https://doi.org/10.1109/ICRITO66076.2025.11241161 https://www.scopus.com/pages/publications/105029803555?origin=resultslist
Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Dept. of Computer Science, Bengaluru, India; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Nwigwe S., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
AI in Predictive HR Analytics for Talent Management
Employee Retention; Employee Turnover; Machine Learning; Predictive HR Analytics; Random Forest; Talent Management; Workforce Optimization
This paper presents the topic on how Machine Learning (ML) can be used to conduct Predictive HR Analytics to streamline Talent Management practices. The aim of the development of the project is mainly the application of Random Forest as a supervised learning model to forecast turnover of the employees, performance, and career-growth potential. With the historical employee data, such as performance reviews, tenure, and levels of engagement, Random Forest models would help determine the aspects that are significant factors to employee retention and performance. The model is incorporated with HR software solutions such as SAP SuccessFactors that help to gather information seamlessly to make predictions in real-time, and base decisions on data. It can be seen in the findings of this research that this approach to identifying the factors that influence the effort to retain employees based on the likelihood of them leaving was not only more accurate than other methods but much more effective in the retention efforts. Through predictive analytics, organizations are better placed to take the initiative of managing talent, minimizing turnover and streamline workforce productivity, which eventually lead to business success. This research demonstrates that such predictive models based on AI have a high potential to change HR practice. 2025 IEEE.
Rajagopal, Navaneetha Krishnan; Chib, Shiney; Singh, Anjali; Sasidharan, M.; Chacko, Elizabeth; Nargunde, Amarja Satish
Proceedings - 2025 International Conference on Recent Innovation in Science Engineering and Technology, ICRISET 2025;
https://doi.org/10.1109/ICRISET64803.2025.11254762 https://www.scopus.com/pages/publications/105031412748?origin=resultslist
Rajagopal N.K., University of Technology and Applied Sciences, College of Economics and Business Administration, P.O. Box 608, Salalah, 211, Oman; Chib S., Datta Meghe Institute of Management Studies, Rtmnu, Nagpur, India; Singh A., Gl Bajaj College of Technology and Management, Dms, Greater Noida, India; Sasidharan M., Ck College of Engineering and Technology, Department of Mba, Cuddalore, 607001, India; Chacko E., Christ University, Dharmaram College Post, Hosur Road, Karnataka, Bengaluru, 560029, India; Nargunde A.S., Bharati Vidyapeeth (Deemed to be University), Institute of Management and Rural Development Administration, Department of Management Studies, Maharashtra, Sangli, India
Cognitive IoT-Integrated Real-Time Transaction Monitoring System for Financial Security
Anomaly Detection; Cognitive IoT; Feature Selection; Federated Learning; Financial Security; LSTM-Autoencoder; Real-Time Monitoring
As financial transactions become more complex in terms of the digitally connected environment, smart and real-time monitoring systems are required to fight off fraud and guarantee safety. The paper proposes a Cognitive IoT based Real-Time Transaction Monitoring System which is a proposed system to the financial sector. The proposed model resorts to the Z-Score normalization as a means of pre-processing the data and Recursive Feature Elimination with Random Forest as a method of selecting the most relevant features affecting the behavior of transactions. As the main type of classifier, a hybrid LSTM-Autoencoder model is used that makes it possible to reliably detect anomalies both using sequential and reconstruction-based learning. Developed on top of TensorFlow and its federated learning extensions, the system maintains the privacy of its user data by supporting decentralized training of edge devices. Experimental tests indicate higher results over the detection accuracy, which reduces false positives, and real-time reactivity. It is a scalable, safe, flexible system that can be used to monitor financial transactions efficiently, using the combined potential of the cognitive computer, the IoT environment, and powerful machine learning to respond to the dynamic nature of financial security issues. 2025 IEEE.
Hemalatha Yadav, J.; Arora, Kapil; Salgotra, Priyanka; Dawod, NajimAubed; Patil, Amit Kumar; Kataria, Abhinav
https://doi.org/10.1109/ICRISET64803.2025.11251638 https://www.scopus.com/pages/publications/105031406686?origin=resultslist
Hemalatha Yadav J., Alliance University, Bangalore, India; Arora K., Alliance University, Alliance School of Business, Department of Management, Bangalore, India; Salgotra P., Maharishi Markandeshwar (Deemed to be) University, Maharishi Markandeshwar Institute of Management, (Ambala) Haryana, Mullana, India; Dawod N., Al-Nisour University College, Department of Medical Laboratories, Nisour Seq. Karkh, Baghdad, Iraq; Patil A.K., Mit Art, Design and Technology University, Department of Ece, Pune, India; Kataria A., Christ Deemed to be University, Delhi NCR Campus, India
Market-Based Strategies for Enhancing Grid Resilience under High Renewable Penetration
Ancillary services; Flexibility services; Grid resilience; Particle Swarm Optimization (PSO); Renewable energy integration
The rise in penetration of renewable energy sources (RES), especially wind and solar, presents important challenges to power system operation and electricity markets. Renewables cut emissions and are cheaper in the long run but are intermittent, creating volatility, uncertainty about revenues, and reliability risks. This study analyses the performance of markets in delivering resilience to the grid given high levels of renewables. A grid simulation over 24 hours was then developed in order to study four aspects of the problem; (i) Stability of revenues for the generators, (ii) Reduction in the cost of imbalances as a result of flexibility, (iii) Reliability in terms of Loss of Load Expectation (LOLE), and Energy Not Served (ENS), and (iv) the impact on costs for consumers. Findings show that flexibility participation reduces costs incurred due to imbalance by ? 60%, and resilience mechanisms reduce the effective tariff faced by consumers from 32/kWh to 6.8/kWh. Reliability indices also improve greatly when flexibility and ancillary services markets are introduced. In addition, a Particle Swarm Optimization (PSO) model was applied to find the optimum levels of renewable penetration and flexibility that result in the least total system cost, which correspond to approximately 0.7 for flexibility and approximately 55-65% for renewables. These results point to the economic and technical need for reengineered electricity markets that incorporate flexibility products, ancillary services, and resilience incentives. This dissertation provides a complete methodology of simulation, reliability metrics, and optimization to inform policy makers, system operators, and investors of resilient renewable dominated grids. 2025 IEEE.
Ghouse, Syed Mohammad; Nagaraju, E.; Prakash, Om; Zabiullah, Ismail; Gowda, Anil B; Sharief, Mohammed Ismail
14th International Conference on Renewable Energy Research and Applications, ICRERA 2025;pp.1848-1853
https://doi.org/10.1109/ICRERA66237.2025.11283943 https://www.scopus.com/pages/publications/105032835369?origin=resultslist
Ghouse S.M., CMR University, School of Management, Karnataka, Bangalore, India; Nagaraju E., CMR University, School of Management, Karnataka, Bangalore, India; Prakash O., CMR University, School of Management, Karnataka, Bangalore, India; Zabiullah I., CMR University, School of Management, Karnataka, Bangalore, India; Gowda A.B., Christ University, Karnataka, Bangalore, India; Sharief M.I., CMR University, School of Management, Karnataka, Bangalore, India
Counterfactual Demand Forecasting Using Multivariate LSTM
counterfactual analysis; Demand forecasting; multivariate LSTM; retail time series; weather-aware forecasting
Demand forecasting is a key part of running operations efficiently in the fast-changing retail and online shopping industries. Regular methods that use statistics often have trouble handling the complex, changing, and time-based patterns found in actual sales data. This study introduces a new way to predict demand that uses multivariate Long Short-Term Memory (LSTM) models. The models take both the order of sales over time and other factors like prices and weather into account. Three model designs were tested: a simple straightforward model, a pure LSTM model, and a new hybrid LSTM model that mixes time-based data with steady economic factors. The combined hybrid model worked the best, by successfully balancing learning from sequences with keeping things stable. The study did experiments to see what would happen if weather conditions changed, like extreme heat, cold, storms, or dry spells and compared normal forecasts with these changed scenarios to see how demand would shift for products and overall sales. The results show that this new framework not only makes better predictions but also gives useful information on how weather events can affect store sales. By linking prediction with 'what if' analysis, this research moves demand forecasting from just predicting what will happen to helping make better decisions. 2025 IEEE.
Khatija, Liya; Vinoth, Dalvin; Kavitha, R.
Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.217-221
https://doi.org/10.1109/ICRCICN68210.2025.11365012 https://www.scopus.com/pages/publications/105035365805?origin=resultslist
Khatija L., Christ (Deemed-to-be University), Bengaluru, India; Vinoth D., Christ (Deemed-to-be University), Bengaluru, India; Kavitha R., Christ (Deemed-to-be University), Bengaluru, India
Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning
ARCANE; Clustering; K-means++; Machine Unlearning
To address the demand for exact data removal in unsupervised clustering, a novel framework for exact machine unlearning is proposed that integrates the K-Means++ algorithm with ARCANE. This framework combines high-quality cluster initialization with targeted partitioning, allowing a more efficient method for removing data without the need for a naive retraining of the model. The proposed model is compared to a SISA-based approach against synthetic and Iris datasets. The ARCANE K-Means++ model demonstrated superior clustering quality, achieving a Silhouette Score of 0.841 to the baseline's performance of 0.263. ARCANE framework also demonstrated better speedup and predictable unlearning times for typical deletion requests than the SISA model. This is a strong, scalable, and provably-exact method for machine unlearning, providing a new and intuitive framework for developing privacy-preserving AI. 2025 IEEE.
Gupta, Shrutika; Naskar, Annesha; Misra, Bitan; Hemanth, K.S.; Chakraborty, Sayan; Kulkarni, Aparna Shrikant
Proceedings of the International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN;Issue;2025;pp.418-423
https://doi.org/10.1109/ICRCICN68210.2025.11364795 https://www.scopus.com/pages/publications/105035367086?origin=resultslist
Gupta S., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Naskar A., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Misra B., Techno International New Town, Department of Computer Science and Engineering, West Bengal, India; Hemanth K.S., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Chakraborty S., JIS College of Engineering, Department of Computer Science and Technology, West Bengal, Kalyani, India; Kulkarni A.S., MIT Academy of Education, Alandi, Department of Computer Science and Engineering (Data Science), Pune, India
Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey
Apriori; Data Mining; Frequent Pattern Mining; Matrix-based Apriori
Data Mining identifies intriguing, useful, and previously unknown patterns and correlations between data stored in databases or warehouses. Frequent Pattern Mining (FPM) is one of the vital methods in the prospering arena of data mining (DM), and it describes the relationship between the items in the datasets. In the last two decades, many studies were carried out in FPM using the Apriori algorithm. The Apriori algorithm requires many database scans and produces numerous candidate itemsets, increasing I/O cost and decreasing computational efficiency. To address these issues, researchers contributed many improved versions of Apriori and proved that those algorithms scan the database only once and identify the frequent itemsets quickly, especially when the itemsets are higher, and provide higher efficiency and feasibility. This research article summarizes matrix-based Apriori algorithms in the literature used for identifying frequent itemsets. 2025 IEEE.
Kalpana, P.; Sumathi, P.; Jose, Teena; Deepa, S.; Gondkar, Raju Ramakrishna; Zeema, Loveline J.
3rd International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2025 - Proceedings;pp.73-78
https://doi.org/10.1109/ICRAIS66073.2025.11234713 https://www.scopus.com/pages/publications/105029752804?origin=resultslist
Kalpana P., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Sumathi P., Dept. of Computer Science, Vysya College, Tamil Nadu, Salem, India; Jose T., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Deepa S., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Gondkar R.R., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Zeema L.J., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India
Flowing Blood Analysis & Separation: Integrating Raman Spectroscopy & Acoustophoresis in a Microfluidic System
Acoustophoresis; Blood Cell Separation; Long Short-Term Memory Recurrent Neural Networks; Microfluidic Systems; Raman Spectroscopy
The proposed system combines Raman Spectroscopy and Acoustophoresis in a microfluidic environment to provide label-free analysis and separation of the cells present in the blood, including red blood cells, white blood cells and platelets. Based on the Raman Effect, Raman Spectroscopy captures crucial information about the structural and vibrational traits of the blood sample, exposing the molecules to monochromatic radiation and recording their Raman shift. This data is processed using LSTM recurrent neural networks, creating a deep-learning framework for species identification and quantification. The model can forecast the cell population in the blood sample and obtain information on the size and compressibility of individual cells due to the microfluidic nature of the data received by the Raman Spectroscope. Raman spectra allow estimation of the cell size and compressibility, guiding cell separation using acoustophoresis and allowing adjustments of the acoustic wave frequency based on the estimated size and compressibility of the cells. The targeted cells are directed toward a designated collection chamber within the microfluidic system by dynamically adjusting the acoustic wave frequency. Repeated application allows complete separation of the cells and pressure-driven delivery of the targeted cells to their respective collection reservoirs. 2025 IEEE.
Rawal, Shorya; Sinha, Shambhavi; Jayapriya, J.; Vinay, M.
Proceedings of the 2025 3rd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2025;
https://doi.org/10.1109/ICPEEV67897.2025.11291246 https://www.scopus.com/pages/publications/105032375308?origin=resultslist
Rawal S., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Sinha S., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India
Exploring Emerging AI and IoT Technologies to Enhance Customer Relationship Management in 5G Networks
5G Network; Artificial Intelligence; customer relationship management; Education; Internet of Things
The convergence of artificial intelligence, Internet of Things for management of 5G networks to revolutionize customer relationship management. It enables customer engagement, efficiency and levels of personalization. The study explores the emergence of IoT and AI to enhance CRM framework within low-latency and high-speed infrastructure provided using 5G technology. Real time data collection with IoT and AI driven data analytics that offer actionable insights to enable customer behaviour, enabling adaptive and predictive CRM strategies. Additionally, enhanced connectivity with 5G technology to facilitate integration of smart devices to ensure interactive and consistent experience for the customers. The present study examined the operational and technical synergy between the technologies and has high impact on the efficiency of CRM, implementation challenges that include data security and privacy concerns. 2025 IEEE.
Sathyavani, Bandela; Poddutoori, Jaipal Reddy; Pawar, Sudarshan A.; Banerjee, Devleena; Muralidhar, L.B.; Kapila, Dhiraj
2025 International Conference on Pervasive Computational Technologies, ICPCT 2025;pp.922-926
https://doi.org/10.1109/ICPCT64145.2025.10941610 https://www.scopus.com/pages/publications/105002848175?origin=resultslist
Sathyavani B., Sr University, School of Engineering, Department of Eee, Telangana, Warangal, India; Poddutoori J.R., Solution Architect Affiliation Bbu, Downingtown, 19335, PA, United States; Pawar S.A., Pes Modern Institute of Business Studies, Marketing Management, Nigdi, Pune, India; Banerjee D., Christ University, India; Muralidhar L.B., School of Commerce, Jain (Deemed to Be University), Department of Management Studies, Karnataka, Bengaluru, India; Kapila D., Lovely Professional University, Department of Computer Science & Engineering, Punjab, Phagwara, India
AI and IoT in Digital Marketing: Enhancing Automation, Personalization, and Consumer Interaction
and Consumer interaction; Artificial intelligence; Digital marketing; Internet of things; Machine learning
An interconnected system of Internet-enabled devices that can collect and transmit statistics via a wireless connection without the assistance of people is known as the Internet of Things (IoTs). Accordingly, the IoT's seductive power is causing significant shifts in the current corporate environment. The digital marketing industry stands to gain the most from this innovation, which is currently causing major shifts in many other sectors. Using a variety of digital marketing strategies, this innovation gathers numerous types of consumer statistics. IoT technological advancements' impact on digital marketing tactics and customer interaction has emerged as a crucial research topic as it begins to pervade many facets of everyday existence. This investigation examines the application of IoT-based machine learning (ML) in digital marketing for the food business. To give ML-based suggestions, consumer data is analyzed, interests are identified, and conduct is predicted using ML approaches. The ensemble technique aggregates the results of multiple ML techniques to produce an individual forecast. The accuracy matrices graphs for the K-nearest neighbor and decision trees produced excellent estimations, with 100% accuracy and 0.0 error, correspondingly. The nae Bayes method achieved 97.2% accuracy with a 0.029 error, successfully identifying the right tags across every category. The guided ensemble of 3 ML methods is demonstrated by effectively enhancing digital marketing tactics in the food distribution industry by reducing duration and expenses. 2025 IEEE.
Naredla, Suman; Singh, Madhulika; Chaturvedi, Nidhi; Banerjee, Devleena; Gobinath, V.M.; Anil Tiwari, Dr.
2025 International Conference on Pervasive Computational Technologies, ICPCT 2025;pp.895-899
https://doi.org/10.1109/ICPCT64145.2025.10941275 https://www.scopus.com/pages/publications/105002869516?origin=resultslist
Naredla S., Sr University, School of Business, Telangana, Warangal, India; Singh M., Quantum University, Computer Science and Engineering, Uttarakhand, Roorkee, India; Chaturvedi N., Emirates Aviation University, Faculty of Business Management, Dubai, United Arab Emirates; Banerjee D., Christ University, India; Gobinath V.M., Rajalakshmi Institute of Technology, Mechanical Engineering Department, Tamilnadu, Chennai, India; Anil Tiwari D., Rnb Global University Bikaner, Department of Commerce and Management, India
Challenges and Opportunities in Deploying Explainable AI for Financial Risk Assessment
credit risk; Explainable AI; Financial risk evaluation; Gradient boosting; Support vector machine
Artificial intelligence (AI) has been used more and more in financial decision-making recently, raising questions about the accountability and transparency of these complex systems. The current study investigates the way Explained Artificial Intelligence (XAI) methods might alleviate these concerns and improve the openness of financial decision-making procedures. Nowadays machine learning algorithms are easier to use than ever before, but creating and deploying systems that facilitate real-world banking services has proved challenging. This is mostly due to the fact that algorithms for machine learning are neither transparent or explainable, two attributes that are essential to creating reliable technology. What sets this study unique is the construction of an explainable artificial intelligence (XAI) model that addresses these accessibility concerns while also serving as an instrument for the establishment of credit risk control policies. This work proposes an explainable artificial intelligence model for financing risk control to measure the risks associated with credit financing via peer-to-peer financing networks. The framework uses Shapley parameters to provide AI forecasts according to significant factors that explain. The Support Vector Machine (SVM) and gradient boosting methods had the greatest accuracy scores, 92.4 and 97.6, accordingly. The accuracy of the model was evaluated on a bigger database, and the findings demonstrated that it regularly achieved high levels of accuracy. The SVM and GBM models achieved accuracies of 94.8 and 97.6, respectively. 2025 IEEE.
Akkalkot, Afsha Imran; Kulshrestha, Nitin; Sharma, Geeti; Singh Sidhu, Kawerinder; Palimkar, Sneha S.; Nethravathi, K.
2025 International Conference on Pervasive Computational Technologies, ICPCT 2025;pp.382-386
https://doi.org/10.1109/ICPCT64145.2025.10940643 https://www.scopus.com/pages/publications/105002849746?origin=resultslist
Akkalkot A.I., College of Engineering Pune Technological University, Computer Science Engineering, Maharashtra, Pune, India; Kulshrestha N., Christ Deemed to Be University, Commerce Finance and Accountancy, Uttar Pradesh, Ghaziabad, India; Sharma G., Jain Deemed to Be University, Faculty of Management Studies (CMS Business School), Bengaluru, India; Singh Sidhu K., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Palimkar S.S., Computer Engineering and Technology Coep Technological University, Maharashtra, Pune, India; Nethravathi K., Bms Coordinator, Jain (Deemed-to-be) University, Bangalore, India
The Impact of AI on High-Frequency Trading: A New Paradigm in Share Market Dynamics
AI; ML; Precision; Share Market; Trading
A fresh approach in financial trading has emerged as a result of the significant shifts in the dynamics of global share markets brought about by the incorporation of artificial intelligence (AI) into high-frequency trading (HFT). In order to analyse large datasets in real-time, perform trades in microseconds, and AI-driven Deep learning, machine learning, and natural language processing are all examples of things that HFT uses to help people make the best choices they can in markets that have to change all the time. This change will help people make better decisions, and sellers will be able to respond quickly to changes in the market. Individuals can trade faster, better, and for greater amounts of cash with it than ever before. AI does have some problems when used in HFT, though. It can make the market less stable, lead to legal problems, and make it more diligently to be fair and honest. The amount of money, how well markets work, along with the way risks are handled are all changed by AI. It also discusses about how AI changes HFT. This study talks about the pros and cons of HFT powered by AI. Along with the way shares are sold, it also hints at how it might change future rules. 2025 IEEE.
Kafila; Hari Krishna, Somanchi; Kulshrestha, Nitin; Singh Sidhu, Kawerinder; Budiharjo, Roy; Inumula, Krishna Murthy
2025 International Conference on Pervasive Computational Technologies, ICPCT 2025;pp.452-458
https://doi.org/10.1109/ICPCT64145.2025.10940362 https://www.scopus.com/pages/publications/105002861705?origin=resultslist
Kafila, Sr University, School of Business, Telangana, Warangal, India; Hari Krishna S., Vignana Bharathi Institute of Technology, Department of Business Management, Aushpur Village, Ghatkesar Mandal, Telangana State, 501301, India; Kulshrestha N., Christ Deemed to Be University, Commerce Finance and Accountancy, Uttar Pradesh, Ghaziabad, India; Singh Sidhu K., Uttaranchal University, Uttaranchal Institute of Management, Uttarakhand, Dehradun, India; Budiharjo R., Telkom University, Accounting, School of Economics and Business, West Java, Bandung, 40257, Indonesia; Inumula K.M., Symbiosis International (Deemed University), Symbiosis Institute of International Business (SIIB), Maharashtra, Pune, 411057, India
Algorithmic Crypto Trading using EMA Strategy
Algorithmic Trading; Automated Systems; Cryptocurrency; Data Visualization; Exponential Moving Average; Portfolio Analytics
Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework. 2025 IEEE.
Revathi, S.; Meesala, Shobha; Sudha, V.; Priyanka, R.; Manikandakumar, M.; Vigneshwaran, T.
Proceedings of 5th International Conference on Pervasive Computing and Social Networking, ICPCSN 2025;pp.997-1002
https://doi.org/10.1109/ICPCSN65854.2025.11035368 https://www.scopus.com/pages/publications/105010204561?origin=resultslist
Revathi S., Cmr Institute of Technology, Bengaluru, India; Meesala S., Reva University, Bangalore, India; Sudha V., Cambridge Institute of Technology, Bangalore, India; Priyanka R., Cambridge Institute of Technology, Bangalore, India; Manikandakumar M., Christ University, Bangalore, India; Vigneshwaran T., Christ University, Bangalore, India
Hybrid Digital Control and Single-Stage Converter Architecture for IoT and Photovoltaic Grid Applications
DC-DC Buck Converter; Digital PWM; Photovoltaic Integration; Renewable Energy Systems; Universal Solar Converter; Zero Current Detector
This work presents a hybrid power conversion strategy that merges high-efficiency digital DC-DC techniques with a universal single-stage DC-DC/AC converter for renewable and low-power applications. From the first concept, a digitally controlled buck converter employing a two-step digital PWM, Adaptive Window ADC, and Self-Tracking Zero Current Detector ensures ultra-low power consumption, minimized output ripple, and improved efficiency, making it suitable for IoT and energy-constrained devices. From the second, a dual-leg single-stage converter architecture enables seamless photovoltaic integration with both DC and AC grids, reducing redundancy through shared semiconductor structures and optimized protection circuits. By unifying these approaches, the proposed system enhances power density, conversion flexibility, and grid adaptability while ensuring reduced switching losses, minimized EMI, and stable operation under varying loads. Simulation and experimental validation confirm efficiency above 91% for low-power DC applications and up to 97% for PV-based grid operation, demonstrating strong potential in sustainable smart energy systems. 2025 IEEE.
Jayakumar, S.; Reddy Cheepati, Kumar; Praveen Kumar, D.; Sita, Hareesh; Arunraja, A.; Venkatapathi, K.
Proceedings of the 6th International Conference on Smart Electronics and Communication, ICOSEC 2025;pp.787-792
https://doi.org/10.1109/ICOSEC67334.2025.11459802 https://www.scopus.com/pages/publications/105037132070?origin=resultslist
Jayakumar S., Sri Sairam College Of Engineering, Bengaluru, India; Reddy Cheepati K., Ksrm College Of Engineering, Electrical And Electronics Engineering, Kadapa, India; Praveen Kumar D., Sri Venkateswara College Of Engineering, Electrical And Electronics Engineering, Tirupati, India; Sita H., Mother Theresa Institute Of Engineering And Technology, Electrical And Electronics Engineering, Palamaner, India; Arunraja A., Christ Deemed To Be University, Bangalore, India; Venkatapathi K., Sri Venkatesa Perumal College Of Engineering And Technology, Electrical And Electronics Engineering, Puttur, India