Predicting Bank Customer Churn: An XGBoost Approach to Enhancing Customer Retention Ruchika Bhuria, Srinivas Aluvala Proceedings 3rd International Conference on Advancement in Computation and Computer Technologies Incacct 2025, 2025 This study applies the XGBoost classifier to analyze the propensity of the banking’s customer churn, by analyzing the dataset containing the basic demographics, financial records, and churn label. Some of the features captured in the dataset include; Surname, Row Number, CustomerID, Credit Score, Gender, Age, Tenure, Account Balance, Credit Card, Number of Products, Geometric Mean, Active Member No., Projected Salary, Exited where Exited = 1, the customer has left the bank. The purpose of present research is to apply the predictive analytics in order to understand what underlying factors serve as indicators of customer attrition and in turn facilitate the better strategies of the relationships’ maintenance. The model scores an average accuracy of 85%, based on 2000 samples, relatively well for predicting non-churning customers (Class 0) with an accuracy of 0.90, recall of 0.93, and an F1 of 0.91. Nevertheless, low accuracy (0,65), recall (0,56), and F1-score (0,60) were received for the churned customers (class 1), which means a high level of false negatives. These results also reveal that the task of identifying churned customers is indeed difficult especially when working with imbalanced data sets. Thus, the macro average performance shows that the model is not very good at generalizing, especially in the case of the minority class. This paper suggests that there is a need for further model improvement including hyperparameters tuning and implementing ways to address the imbalance of class problem to enhance sensitivity in detecting churned customers. Improving the model’s accuracy at identifying ‘at risk’ customers will go a long way to helping refining customer retention strategies for the banking industry, thus helping cut attrition rates and increase overall satisfaction.
Towards Sustainable Agriculture: Mango Leaf Disease Classification with Deep Learning Models Srinivas Aluvala, Shanvi Chauhan 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025 This work proposes a deep learning-based method using the EfficientNetB5 model for the classification of mango leaf images into eight categories, including six disease classes (Powdery Mildew, Cutting Weevil, Anthracnose, Bacterial Canker, Sooty Mould, and Gall Midge), a healthy class, and Die Back. Training, validation, and test sets were formed from a publicly available collection comprising 4,000 labeled images. Using Adamax optimizer with hyperparameters including a batch size of 32, a learning rate of 0.001, and a dropout rate of 0.2, the model was fine-tuned. Supported by high precision, recall, and F1-scores across all classes, data augmentation techniques and an input image size of (224,224,3) helped the model, to achieve an amazing test accuracy of 97%. Minimal misclassification revealed by the confusion matrix analysis confirmed the model's consistency in disease diagnosis. This work conforms with the objectives of sustainable agriculture and responsible production through exact disease identification, so enhancing food security and supporting economic development in agricultural communities. For more general relevance in agricultural activities, future developments could be real-time deployment using mobile apps.
Classifying Multiple Skin Infections using Machine Learning Anmol Rattan Singh, Gurjinder Singh, Srinivas Aluvala, Saif Obbayed 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025
Automated Tulsi Leaf Disease Classification Using a Fine-Tuned ResNet50-Based Deep Convolutional Network G Verma, S Aluvala, N Sharma, S Gupta, V Aarwal 2026 IEEE International Conference on Interdisciplinary Approaches in … , 2026 2026
Deep Learning-Based Approach for Classification of Cherry Leaf Diseases Using CNN Model R Singh, S Aluvala, N Sharma, S Gupta, V Agarwal 2026 Contemporary Computing Innovations Conference (CCIC), 1-6 , 2026 2026
Smart Poultry Health Surveillance: A Deep Learning Model for Chicken Disease Detection from Fecal Samples R Singh, S Aluvala, N Sharma, S Gupta, V Agarwal 2026 Contemporary Computing Innovations Conference (CCIC), 1-6 , 2026 2026
A ResNet50-based Deep Learning Model for Accurate Malaria Parasite Detection from Blood Smear Images R Singh, S Aluvala, S Gupta, V Aarwal 2025 6th International Conference on IoT Based Control Networks and … , 2025 2025
Automated Recognition of Wheat Leaf Diseases through Fine-Tuned VGG16 in Smart Agriculture S Aluvala, G Verma, S Gupta, V Aarwal 2025 6th International Conference on IoT Based Control Networks and … , 2025 2025
Deep Learning-based Classification of Satellite Images using Fine-Tuned DenseNet201 S Aluvala, S Bhatt, A Joshi 2025 International Conference on NexGen Networks and Cybernetics (IC2NC … , 2025 2025
AI-Driven Intrusion Detection in Software-Defined Networks Using GNNs C Thumma, DV Patil, S Dash, M Balakrishnan, S Aluvala 2025 3rd International Conference on Computational Intelligence and Network … , 2025 2025
Ethical Considerations in AI-Driven Surveillance Systems C Thumma, AS Sribhashyam, S Kadari, M Bhende, H Sharma, S Aluvala 2025 3rd International Conference on Computational Intelligence and Network … , 2025 2025
AI-Powered Solutions for Real-Time Speech Enhancement in VoIP Communication Networks P Koduru, K Nagalatha, S Garlapati, G Supriya, S Aluvala 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025 2025
Performance Analysis of Different Substrates Used in Next Generation Antenna PK Malik, S Khera, PC Srivastava, A Rahim, S Aluvala, G Krishna 2025 5th International Conference on Advancement in Electronics … , 2025 2025
Efficient Rice Grain Classification Using MobileNetV2 Architecture S Aluvala, G Verma, S Gupta, V Aarwal 2025 2nd Global AI Summit-International Conference on Artificial … , 2025 2025
A Deep Learning Approach for Binary Classification of Bone Fractures in Multi-Region X-ray Images S Aluvala, S Bhatt, A Joshi 2025 2nd Global AI Summit-International Conference on Artificial … , 2025 2025
SmartTrack: Online RFID Card Attendance System using Ardiuno UNO AK Singh, AS Chauhan, A Mahajan, A Rahim, S Aluvala, PK Malik 2025 International Conference on Intelligent and Secure Engineering … , 2025 2025
Telecommunication Growth and Business Opportunities in India's Evolving Market: Trends, Investment, and Future Prospects A Naim, N Ayedee, A Kumar, PK Malik, S Aluvala, S Thapliyal 2025 International Conference on Electronics, AI and Computing (EAIC), 1-5 , 2025 2025
Optimizing weather pattern recognition with MobileNetV3 architecture S Aluvala, MK Goel, G Singh 2025 6th International Conference for Emerging Technology (INCET), 1-5 , 2025 2025 Citations: 1
Automated Malaria Detection Using Deep Learning and the Lacuna Dataset P Nasra, S Gupta, GR Kumar, S Aluvala 2025 3rd International Conference on Advancement in Computation & Computer … , 2025 2025 Citations: 8
Predicting Bank Customer Churn: An XGBoost Approach to Enhancing Customer Retention R Bhuria, S Aluvala 2025 3rd International Conference on Advancement in Computation & Computer … , 2025 2025 Citations: 1
A Comprehensive Approach to Vehicle Classification with Deep Learning Techniques S Chauhan, S Aluvala, MI Habelalmateen 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025
Enhancing orange crop health: Disease detection using MobileNetV2 model AR Singh, S Aluvala 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025 Citations: 2
Vegetable Classification Using Pretrained Deep Learning Model for Accurate Identification MK Goel, G Singh, S Aluvala 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Deepfake generation and detection: Case study and challenges Y Patel, S Tanwar, R Gupta, P Bhattacharya, IE Davidson, R Nyameko, ... IEEE Access 11, 143296-143323 , 2023 2023 Citations: 232
Explainable AI for industry 5.0: Vision, architecture, and potential directions C Trivedi, P Bhattacharya, VK Prasad, V Patel, A Singh, S Tanwar, ... IEEE Open Journal of Industry Applications 5, 177-208 , 2024 2024 Citations: 106
Confluence of Machine Learning with Edge Computing for IoT Accession K Mannanuddin, S Aluvala, Y Sneha, E Kumaraswamy, E Sudarshan, ... IOP Conference Series: Materials Science and Engineering 981 (4), 042003 , 2020 2020 Citations: 71
Concurrences of deep learning arise in analysis of bigdata V Sivalenka, S Aluvala, Y Sneha, K Mannan, S Farheen, K Mahender AIP Conference Proceedings 2418 (1), 020057 , 2022 2022 Citations: 34
An Empirical Study of Routing Attacks in Mobile Ad-hoc Networks S Aluvala, KR Sekhar, D Vodnala Procedia Computer Science 92, 554-561 , 2016 2016 Citations: 34
A Novel Technique for Node Authentication in Mobile Ad hoc Networks S Aluvala, KR Sekhar, D Vodnala Perspectives in Science 8, 680-682 , 2016 2016 Citations: 32
Design a Cost Optimum for 5g Mobile Cellular Network Footing on NFV and SDN BV Kumar, Y Chanti, N Yamsani, S Aluvala, B Bhaskar International Journal of Recent Technology and Engineering (IJRTE) ISSN … , 2019 2019 Citations: 30
Dynamic resource allocation-enabled distributed learning as a service for vehicular networks T Ganesan, RR Al-Fatlawy, S Srinath, S Aluvala, RL Kumar 2024 Second International Conference on Data Science and Information System … , 2024 2024 Citations: 28
Intrusion detection in industrial Internet of Things based on recurrent rule-based feature selection MV Devarajan, S Aluvala, V Armoogum, S Sureshkumar, HT Manohara 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 25
IoT based saline level monitoring system G Sunil, S Aluvala, G Ranadheer Reddy, V Sreeharika, P Sindhu, ... IOP Conference Series: Materials Science and Engineering 981 (3), 032095 , 2020 2020 Citations: 25
Geetanjali and A PK Malik, AS Duggal, S Aluvala, R Sahithi Gehlot,“Development of a low-cost IoT device using ESP8266 and Atmega328 for … , 2023 2023 Citations: 21
Data mining for predictive analytics and optimization of treatment plans in cardiovascular disease management using neural networks S Kaliappan, V Balaji, GB Bharathi, S Aluvala 2024 International Conference on Advancements in Smart, Secure and … , 2024 2024 Citations: 20
Development of a low-cost IoT device using ESP8266 and Atmega328 for real-time monitoring of Outdoor Air Quality with Alert PK Malik, AS Duggal, S Aluvala, R Sahithi, A Gehlot 2023 3rd International Conference on Advancement in Electronics … , 2023 2023 Citations: 19
Unlocking the power of natural language processing through journaling with the assistance AK Mishra, KK Bhartiy, J Singh, S Aluvala, P Singh, K Kishor 2023 3rd International Conference on Innovative Sustainable Computational … , 2023 2023 Citations: 16
BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging S Chauhan, R Cheruku, D Reddy Edla, L Kampa, SR Nayak, J Giri, ... Frontiers in physiology 15, 1349111 , 2024 2024 Citations: 15
Security Enhancement of Genome Sequence Data in Health Care Cloud SSY G.Sunil, Srinivas Aluvala, Nagendar Yamsani, Kanekonda Ravi Chythanya International Journal of Advanced Trends in Computer Science and Engineering … , 2019 2019 Citations: 15
An efficient backbone based quick link failure recovery multicast routing protocol D Vodnala, SP Kumar, S Aluvala Perspectives in Science 8, 135-137 , 2016 2016 Citations: 14
Efficient spectrum sharing in 5G and beyond network: a survey A Gupta, R Kausar, S Tanwar, A Alabdulatif, V Vimal, S Aluvala Telecommunication Systems 88 (1), 29 , 2025 2025 Citations: 12
Revolutionizing Accounting with Blockchain Technology for Enhanced Security and Efficiency V Ahmad, M Arora, R Kumar, S Aluvala, A Vishnoi, L Goyal 2024 3rd International Conference on Sentiment Analysis and Deep Learning … , 2024 2024 Citations: 12
Artificial intelligence is revolution or devolution for employability P Devi, H Kaur, R Kumar, S Aluvala, S Singh 2023 3rd International Conference on Innovative Mechanisms for Industry … , 2023 2023 Citations: 12