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Open AccessEditorial

Preface to the First International Conference on Computational Intelligence and Soft Computing (CISCom 2025)

by Sameena Pathan and Saad Hassan Kiani

Comput. Sci. Math. Forum 2025, 12(1), 5; https://doi.org/10.3390/cmsf2025012005 - 19 Dec 2025

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1 pages, 132 KB

Open AccessEditorial

Statement of Peer Review

by Sameena Pathan and Saad Hassan Kiani

Comput. Sci. Math. Forum 2025, 12(1), 19; https://doi.org/10.3390/cmsf2025012019 - 9 Feb 2026

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15 pages, 2426 KB

Open AccessProceeding Paper

Scalable Machine Learning Solutions for High-Volume Financial Transaction Fraud Detection

by Sourav Yallur, Jiya Patil, Tanvi Shikhari, Prajwal Dabbanavar, Rajashri Khanai and Salma Shahpur

Comput. Sci. Math. Forum 2025, 12(1), 1; https://doi.org/10.3390/cmsf2025012001 - 17 Dec 2025

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More reliable and intelligent detection systems are required because of the rise in fraudulent activities brought on by the volume of digital financial transactions. In this work, the data used is from a publicly accessible dataset with more than a million transaction records [...] Read more.

More reliable and intelligent detection systems are required because of the rise in fraudulent activities brought on by the volume of digital financial transactions. In this work, the data used is from a publicly accessible dataset with more than a million transaction records to investigate a machine learning strategy to identify hidden patterns in the fraud transaction. Data preprocessing included applying Z-score normalization, eliminating outliers using the IQR method, and handling missing values according to the skewness of each attribute. The selection of important features was guided by correlation analysis using Chi-square tests and Pearson coefficients. This study implemented multiple supervised learning techniques, comprising Random Forest, Logistic Regression, K-Nearest Neighbors, and Gradient Boost to evaluate and compare their effectiveness in accurately detecting fraudulent transactions. Full article

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13 pages, 542 KB

Open AccessProceeding Paper

Big Tech and the Sustainable Consumer Practices: A Critical Analysis Using a Mixed Methodology

by Bharti Singh, Anand Pandey and Timsy Kakkar

Comput. Sci. Math. Forum 2025, 12(1), 2; https://doi.org/10.3390/cmsf2025012002 - 17 Dec 2025

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The research is centered on how India’s top-tier IT companies—the “Big Six” of TCS, Infosys, HCLTech, Wipro, Cognizant, and Tech Mahindra—are integrating sustainability in their digitally driven operations, platforms, and business models. The study employs a mixed methodology, combining critical case study analysis [...] Read more.

The research is centered on how India’s top-tier IT companies—the “Big Six” of TCS, Infosys, HCLTech, Wipro, Cognizant, and Tech Mahindra—are integrating sustainability in their digitally driven operations, platforms, and business models. The study employs a mixed methodology, combining critical case study analysis with Fuzzy Delphi validation to assess triangular fuzzy numbers, centroid-based defuzzification, and consensus thresholds. The study explores how AI, big data, analytics, and digital marketing influence environmentally sustainable consumption behaviors within global ecosystems. Results show that, despite limited consumer control, these companies shape sustainability-related behavior indirectly through backend systems, digital platforms, and algorithmic logic—known as “invisible architecture”. This study confirms six main sustainability factors through expert consensus. Noteworthy among those are Digital Infrastructure for Sustainability, Platform Logic for Behavioral Change, and AI-Enabled Analytics and Recommendations. Thematic cross-case results reveal both the promise and ethical challenges of digital sustainability, including the prevalence of greenwashing and risks of overconsumption. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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10 pages, 791 KB

Open AccessProceeding Paper

Data-Driven Approach for Asthma Classification: Ensemble Learning with Random Forest and XGBoost

by Bhavana Santosh Pansare, Anagha Deepak Kulkarni and Priyanka Prabhakar Pawar

Comput. Sci. Math. Forum 2025, 12(1), 3; https://doi.org/10.3390/cmsf2025012003 - 17 Dec 2025

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Across the world, asthma is a prominent and widespread respiratory disorder that has a substantial clinical and socioeconomic influence. The classification of asthma subtypes should be performed precisely and effectively, with objectives such as personalized treatments, improved rehabilitation outcomes, and preventing tragic exacerbations. [...] Read more.

Across the world, asthma is a prominent and widespread respiratory disorder that has a substantial clinical and socioeconomic influence. The classification of asthma subtypes should be performed precisely and effectively, with objectives such as personalized treatments, improved rehabilitation outcomes, and preventing tragic exacerbations. Typical screening approaches are primarily based on spirometry measures, immunologic assessments, and individual clinical diagnoses, and they are commonly affected by limitations such as uncertainty, crossover disparities, and restricted generalizability among various groups of patients. This study utilizes machine learning (ML) methodologies as a Data-Driven Approach (DDA)-based framework for asthma classification to overcome the mentioned challenges. Methodically constructed and evaluated classifiers, such as Random Forest and XGBoost, use the Asthma Disease Dataset from Kaggle, which consists of demographic data, lung function metrics (FEV1, FVC, FEV1/FVC ratio, and PEFR), and immunoglobulin E (IgE) biomarkers. A wide range of metrics such as accuracy, precision, recall, F1-score, receiver operating characteristic area under the curve (ROC-AUC), and average precision (AP) are used exhaustively to assess the performance of the model. The results indicate that though each model exhibits outstanding forecasting abilities, XGBoost has an enhanced classification capability, especially in recall and AP, which minimizes the proportion of false negatives, resulting in a clinically noteworthy result. The significance of the FEV1/FVC ratio, IgE levels, and PEFR as key indicators is recognized by feature interpretability analysis. These results emphasize the ability of ML-powered evaluation in advancing personalized healthcare and revolutionizing the clinical management of asthma. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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8 pages, 446 KB

Open AccessProceeding Paper

Enhanced Early Detection of Epileptic Seizures Through Advanced Line Spectral Estimation and XGBoost Machine Learning

by K. Rama Krishna and B. B. Shabarinath

Comput. Sci. Math. Forum 2025, 12(1), 4; https://doi.org/10.3390/cmsf2025012004 - 17 Dec 2025

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This paper proposes a fast epileptic seizure detection method to allow for early clinical intervention. The primary goal is to enhance computational and predictive performance to make the method viable for online implementation. An advanced Line Spectral Estimation (LSE)-based method for EEG analysis [...] Read more.

This paper proposes a fast epileptic seizure detection method to allow for early clinical intervention. The primary goal is to enhance computational and predictive performance to make the method viable for online implementation. An advanced Line Spectral Estimation (LSE)-based method for EEG analysis was developed with Bayesian inference and Toeplitz structure-based fast inversion with Capon and non-uniform Fourier transforms to reduce computational requirements. XGBoost classifier with parallel boosting was employed to increase prediction performance. The method was tested with patients’ EEG data using multiple embedded Graphic Processing Unit (GPU) platforms and achieved 95.5% accuracy, and 23.48 and 33.46 min average and maximum lead times before a seizure, respectively. The sensitivity and specificity values (92.23% and 93.38%) show the method to be reliable. The integration of LSE and XGBoost can be extended to create an efficient and practical online seizure detection and management tool. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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13 pages, 2431 KB

Open AccessProceeding Paper

Gender-Aware ADHD Detection Framework Combining XGBoost and FLAML Models: Exploring Predictive Features in Women Advancing Personalized ADHD Diagnosis

by Srushti Honnangi, Anushri Kajagar, Shashank Shetgeri, Tanvi Korgaonkar, Salma Shahapur and Rajashri Khanai

Comput. Sci. Math. Forum 2025, 12(1), 6; https://doi.org/10.3390/cmsf2025012006 - 18 Dec 2025

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A machine learning architecture is introduced to predict attention deficit hyperactivity disorder (ADHD) and biological sex from multimodal inputs. The problem sidesteps the clinical task of early ADHD detection and adds prediction of sex as a meta-feature to enhance robustness. The architecture is [...] Read more.

A machine learning architecture is introduced to predict attention deficit hyperactivity disorder (ADHD) and biological sex from multimodal inputs. The problem sidesteps the clinical task of early ADHD detection and adds prediction of sex as a meta-feature to enhance robustness. The architecture is applied to demographic profiles, quantitative tests, and functional brain connectomes as 200 × 200 matrices. Preprocessing includes data harmonization, matrix symmetrization, graph-based descriptor extraction, including total strength, mean, and standard deviation, categorical encoding, variance thresholding, and imputation of missing values using k-nearest neighbors. Sex classification is performed using XGBoost with stratified cross-validation to generate probability outputs that enhance the ADHD model. ADHD classification is tuned using FLAML’s automatic hyperparameter search for XGBoost and class-weighting to address imbalance. Findings show that combining imaging-derived features and automated model selection yields a robust method of ADHD detection, underscoring the utility of multimodal data fusion in neuropsychiatric studies. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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8 pages, 910 KB

Open AccessProceeding Paper

Deep Learning Approaches to Chronic Venous Disease Classification

by Ankur Goyal, Vikas Honmane, Kumarsagar Dange and Shiv Kant

Comput. Sci. Math. Forum 2025, 12(1), 7; https://doi.org/10.3390/cmsf2025012007 - 18 Dec 2025

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Millions of people suffer from chronic venous disease (CVD), a common vascular condition that frequently causes pain, edema, and skin ulcers. For treatment to be effective, its stages must be accurately and promptly classified. This study offers a deep learning-based framework for classifying [...] Read more.

Millions of people suffer from chronic venous disease (CVD), a common vascular condition that frequently causes pain, edema, and skin ulcers. For treatment to be effective, its stages must be accurately and promptly classified. This study offers a deep learning-based framework for classifying CVD stages using medical images, such as limb photos or ultrasound scans. For training and assessment, convolutional neural networks (CNNs) are used in conjunction with pre-trained models like ResNet, VGG, and Efficient Net. Metrics like accuracy, precision, recall, and F1-score are used to evaluate the model’s performance. The encouraging findings suggest that deep learning tools can greatly facilitate the diagnosis of CVD and may be integrated into clinical decision support systems for quicker, more precise evaluations. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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9 pages, 1165 KB

Open AccessProceeding Paper

LSTM-Based News Article Category Classification

by Yusra Rafat, Potu Narayana, R. Madana Mohana and Kolukuluri Srilatha

Comput. Sci. Math. Forum 2025, 12(1), 8; https://doi.org/10.3390/cmsf2025012008 - 18 Dec 2025

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A substantial amount of data is generated day-to-day, to which news articles are a major contributor. Most of this data is not well-structured, highlighting the need for efficient ways to manage, process, and analyze said data. One useful approach involves the categorization of [...] Read more.

A substantial amount of data is generated day-to-day, to which news articles are a major contributor. Most of this data is not well-structured, highlighting the need for efficient ways to manage, process, and analyze said data. One useful approach involves the categorization of the data. The work “News Article Category Classification” develops a Long Short-Term Memory (LSTM) model for classifying news articles into 14 categories. LSTM networks are suitable for text classification tasks, as they efficiently capture contextual and sequential dependencies. They have a special ability to retain long-term information which makes them perfect for understanding the meaning of news articles. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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11 pages, 945 KB

Open AccessProceeding Paper

Self-Supervised Learning for Complex Pattern Interpretation in Vitiligo Skin Imaging

by Priyanka Pawar, Anagha Kulkarni, Bhavana Pansare, Prajakta Pawar, Prachi Bahekar and Madhavi Kapre

Comput. Sci. Math. Forum 2025, 12(1), 9; https://doi.org/10.3390/cmsf2025012009 - 18 Dec 2025

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Depigmented patches are the result of vitiligo, a skin condition brought on by the slow breakdown of melanocytes. High variability, complex lesion morphology, and subtle differences between affected and unaffected skin make accurate diagnosis difficult. In these situations, conventional supervised image analysis techniques [...] Read more.

Depigmented patches are the result of vitiligo, a skin condition brought on by the slow breakdown of melanocytes. High variability, complex lesion morphology, and subtle differences between affected and unaffected skin make accurate diagnosis difficult. In these situations, conventional supervised image analysis techniques have trouble generalizing. By allowing models to acquire significant representations from unlabeled data, self-supervised learning (SSL) presents a viable substitute. The new SSL-based framework for vitiligo skin image analysis proposed in this study uses contrastive learning with augmentation-based pretext tasks to capture complex visual patterns such as patch distribution, texture loss, and border irregularity. The SSL-enhanced model achieved a validation accuracy of 0.83 after fine-tuning on a small, labeled subset. This suggests that SSL could support accurate and labeled efficient vitiligo assessment in clinical and research settings. Direct comparisons with existing supervised model were not performed and were left for future research. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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17 pages, 316 KB

Open AccessProceeding Paper

AI-Powered Cybersecurity Mesh for Financial Transactions: A Generative-Intelligence Paradigm for Payment Security

by Utham Kumar Anugula Sethupathy and Vijayanand Ananthanarayan

Comput. Sci. Math. Forum 2025, 12(1), 10; https://doi.org/10.3390/cmsf2025012010 - 19 Dec 2025

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The rapid expansion of digital payment channels has significantly widened the financial transaction attack surface, exposing ecosystems to sophisticated, polymorphic threat vectors. This study introduces an AI-powered cybersecurity mesh that unites Generative AI (GenAI), federated reinforcement learning, and zero-trust principles, with a forward-looking [...] Read more.

The rapid expansion of digital payment channels has significantly widened the financial transaction attack surface, exposing ecosystems to sophisticated, polymorphic threat vectors. This study introduces an AI-powered cybersecurity mesh that unites Generative AI (GenAI), federated reinforcement learning, and zero-trust principles, with a forward-looking architecture designed for post-quantum readiness. The architecture ingests high-velocity telemetry, coordinates self-evolving agent collectives, and anchors model provenance in a permissioned blockchain to guarantee verifiability and non-repudiation. Empirical evaluations across two production-scale environments—a mobile wallet processing two million transactions per day and a high-throughput cross-border remittance rail—demonstrate a 95.1% threat-detection rate, a 62% reduction in false positives, and a 35.7% latency decrease compared to baseline systems. These results affirm the feasibility of a generative cybersecurity mesh as a scalable, future-proofed blueprint for next-generation payment security. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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9 pages, 941 KB

Open AccessProceeding Paper

A Multiscale Convolutional Neural Network Framework for Automated Segmentation and Pattern Mapping of Psoriatic Lesions

by Anagha Kulkarni, Priyanka Pawar, Bhavana Pansare and Harshal Raje

Comput. Sci. Math. Forum 2025, 12(1), 11; https://doi.org/10.3390/cmsf2025012011 - 19 Dec 2025

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For psoriatic lesions, automatic segmentation is crucial to perform objective assessment, monitoring, and medication planning in dermatology. This study proposes a Multiscale Convolutional Neural Network (MSCNN) framework for precise segmentation of psoriatic lesions from medical images. The model was evaluated using the ISIC [...] Read more.

For psoriatic lesions, automatic segmentation is crucial to perform objective assessment, monitoring, and medication planning in dermatology. This study proposes a Multiscale Convolutional Neural Network (MSCNN) framework for precise segmentation of psoriatic lesions from medical images. The model was evaluated using the ISIC 2017 dataset and demonstrated robust performance in lesion localization. By analyzing the predicted masks, it has been observed that the boundaries closely match ground truth annotations. Metrics for quantitative evaluation are Dice Coefficient, Intersection over Union (IoU), used with high precision and slightly lower recall, reflecting occasional under-segmentation of fine-scale lesion details. The findings focus on the proposed MSCNN’s capability to produce reliable lesion masks, while also detecting areas for advancement in capturing irregular lesion boundaries. Future work involves integrating multimodal imaging, attention mechanisms, and larger, diverse datasets to improve segmentation accuracy and clinical applicability. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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10 pages, 1639 KB

Open AccessProceeding Paper

Machine Learning Framework for Algorithmic Trading

by Krishnamurthy Nayak, Supreetha Balavalikar Shivaram and Sumukha K. Nayak

Comput. Sci. Math. Forum 2025, 12(1), 12; https://doi.org/10.3390/cmsf2025012012 - 22 Dec 2025

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Present financial markets are characterized by great volatility and nonlinear dynamics since they are driven by both quantitative forces and qualitative mood. Traditional trading practices cannot capture such nuance. This study proposes an automated trading system based on machine learning that uses technical [...] Read more.

Present financial markets are characterized by great volatility and nonlinear dynamics since they are driven by both quantitative forces and qualitative mood. Traditional trading practices cannot capture such nuance. This study proposes an automated trading system based on machine learning that uses technical analysis as well as sentiment factors for better decision-making. Historical OHLCV stock price data from 2000 to 2025 was augmented with financial indicators such as SMA, EMA, RSI, and Bollinger Bands, as well as sentiment scores based on real-time news via natural language processing. LightGBM regression for predicting the price range and Histogram-Based Gradient Boosting classification for directional prediction were employed. Signals were generated with volatility-adjusted thresholds and classifier confirmation, and a risk management layer enforced position sizing, stop-loss triggering, and drawdown constraint. Back testing demonstrated improved Sharpe ratio, Sortino ratio, and win rates versus baseline strategies. The findings emphasize that the combination of machine learning and sentiment analysis with risk-conscious design improves predictive accuracy, dependability, and preservation of capital in automated trading systems. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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11 pages, 1114 KB

Open AccessProceeding Paper

A Blockchain-Based Machine Learning Approach for Authentic Healthcare Support Information Systems

by Mudiduddi Lova Kumari, P. S. G. Aruna Sri, Rajapraveen Kumar Nakka, Sonal Sharma, Swaminathan Balasubramanian and Preeti Gupta

Comput. Sci. Math. Forum 2025, 12(1), 13; https://doi.org/10.3390/cmsf2025012013 - 22 Dec 2025

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In the past, health records were primarily on paper and were essential for recording the results of patient information and treatments. The deployment of “electronic health records” (EHRs) is a new development in healthcare that enables authenticated data storage, reliability when accessing data, [...] Read more.

In the past, health records were primarily on paper and were essential for recording the results of patient information and treatments. The deployment of “electronic health records” (EHRs) is a new development in healthcare that enables authenticated data storage, reliability when accessing data, and the establishment of easy communication centralized across healthcare service providers. This change enhances the quality of operations for medical environment decision-making using clinical data and patient involvement. Nevertheless, ensuring the authenticity of “EHRs” is a challenging task as a result of the weaknesses of centralized systems. We, therefore, suggest the implementation of (ABE), particularly (CP-ABE) using the blockchain technique, to overcome this problem. CP-ABE maintains data confidentiality and accuracy by encrypting access policies and smart contracts, thus allowing authorized users to decrypt information based on predetermined attributes. In this way, EHRs are ensured to be unaltered as patients’ privacy is preserved, and healthcare providers are not allowed to evaluate people records without consent. The machine learning techniques (“SVM, RF and Naïve Bayes”) used with datasets like “Cleveland Heart Disease” explain the cause risk factors for speed diagnosis and for cardiac disorders. Such a system not only fortifies the security of EHRs but also provides healthcare professionals with the necessary tools to improve patient care. The use of state-of-the-art encryption methods together with predictive analytics allows healthcare providers to protect patient privacy and at the same time make healthcare delivery more efficient through the use of a clinically informed final judgment of patient and personalized wellness plans. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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10 pages, 1006 KB

Open AccessProceeding Paper

CNN-Based Image Classification of Silkworm for Early Prediction of Diseases

by Kajal Mungase, Shwetambari Chiwhane and Priyanka Paygude

Comput. Sci. Math. Forum 2025, 12(1), 14; https://doi.org/10.3390/cmsf2025012014 - 25 Dec 2025

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The need to automate the disease identification processes is frequent because manual identification is time-consuming and needs professional skills to be performed; hence, it may improve effectiveness and precision. This paper has resolved the problem by using image classification with deep learning to [...] Read more.

The need to automate the disease identification processes is frequent because manual identification is time-consuming and needs professional skills to be performed; hence, it may improve effectiveness and precision. This paper has resolved the problem by using image classification with deep learning to detect silkworm diseases. A Kaggle-sourced dataset of work of 492 labelled samples (247 diseased and 245 healthy) was used with a stratified division into 392 training and 100 testing samples. The transfer learning method was performed on two Residual Network models, ResNet-18 and ResNet-50, in which pretrained convolutional layers were frozen and the last fully connected layer was trained to conduct binomial classification. Performance was measured by standard evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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9 pages, 409 KB

Open AccessProceeding Paper

Smart and Sustainable Infrastructure System for Climate Action

by Bhanu Prakash, Jayanth Sidlaghatta Muralidhar, Mohammed Zaman Pasha, Vijay Kumar Harapanahalli Kulkarni, Shridhar B. Devamane and N. Rana Pratap Reddy

Comput. Sci. Math. Forum 2025, 12(1), 15; https://doi.org/10.3390/cmsf2025012015 - 29 Dec 2025

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Flooding in Bengaluru areas such as Kodigehalli, Hebbal, and Nagavara has led to severe disruptions, including traffic congestion, infrastructure damage, and health risks. To address this issue, we have proposed a smart flood alert and communication system, integrating Internet of things (IoT), artificial [...] Read more.

Flooding in Bengaluru areas such as Kodigehalli, Hebbal, and Nagavara has led to severe disruptions, including traffic congestion, infrastructure damage, and health risks. To address this issue, we have proposed a smart flood alert and communication system, integrating Internet of things (IoT), artificial intelligence (AI), and smart infrastructure solutions. The system helps by giving information about real-time water level sensors, AI-driven flood prediction models, automated emergency coordination, and a mobile-based citizen reporting platform. Through cloud-based data processing, predictive analytics, and smart drainage management, this solution aims to enhance early warnings, reduce emergency response time, and improve urban flood resilience. It yields up to an 80% reduction in alert delays, a 50% faster emergency response, and improved community safety. This project seeks collaboration with government agencies, technology firms, and community stakeholders to implement a pilot plan, ensuring a scalable and sustainable flood mitigation strategy for Bengaluru. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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9 pages, 708 KB

Open AccessProceeding Paper

Visualizing Trends and Correlation Between Fashion Features for Product Design Prediction Using Classification and Sentiment Analysis

by Monika Sharma, Navneet Sharma and Priyanka Verma

Comput. Sci. Math. Forum 2025, 12(1), 16; https://doi.org/10.3390/cmsf2025012016 - 7 Jan 2026

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To demonstrate the interrelation of fashion elements for design forecasting, the research examines classification and sentiment analysis methodologies. The study combines survey data with information from social media and e-commerce sites to find important emotional and behavioral patterns that affect how people make [...] Read more.

To demonstrate the interrelation of fashion elements for design forecasting, the research examines classification and sentiment analysis methodologies. The study combines survey data with information from social media and e-commerce sites to find important emotional and behavioral patterns that affect how people make buying decisions. The research employs deep learning, Logistic Regression, and Random Forest models to predict design trends and user preferences. The research methodology focuses on improving fashion analytics through feature selection and user segmentation and visual storytelling methods to enhance strategic decision-making. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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13 pages, 73269 KB

Open AccessProceeding Paper

Advanced Machine Learning Approaches for Predicting ADHD in Females: A Data-Driven Study Employing the WIDS Dataset

by Parth Patil, Karthik Kamaldinni, Sanjana Patil and Sakshi Gaitonde

Comput. Sci. Math. Forum 2025, 12(1), 17; https://doi.org/10.3390/cmsf2025012017 (registering DOI) - 3 Feb 2026

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Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is found in both children and adults. While this disorder often continues in adulthood, diagnosis can be challenging, particularly in females. Unlike males, who are often diagnosed with ADHD due to their externalizing behaviors [...] Read more.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is found in both children and adults. While this disorder often continues in adulthood, diagnosis can be challenging, particularly in females. Unlike males, who are often diagnosed with ADHD due to their externalizing behaviors (i.e., impulsive nature), most females show inattentive symptoms (i.e., in focusing, disorganization), which makes this disorder hard to detect. This paper proposes a machine learning approach to detect ADHD among females. The Wids Datathon 2025 provides three datasets: categorical data, quantitative data, and function connectomes. It contains information on 1213 participants who are seeking to take a test to detect ADHD. Categorical data includes 10 attributes, quantitative data has 19 attributes, and functional connectomes contain 19,901 attributes which are relevant to studying the participants’ overall condition. By combining both XGBoost and Random Forest, an accuracy of 79.42% was achieved. The results show that machine learning algorithms can help in improving ADHD detection in females, leading to better diagnoses in future. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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12 pages, 826 KB

Open AccessProceeding Paper

A Comprehensive Analysis of Features, Benefits, Challenges, and Best Practices of Security Information and Event Management (SIEM) Solutions

by Marios Vardalachakis, Manos Vasilakis and Manolis Tampouratzis

Comput. Sci. Math. Forum 2025, 12(1), 18; https://doi.org/10.3390/cmsf2025012018 - 6 Feb 2026

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Businesses need good defenses against any number of incidents in the continually evolving area of cybersecurity. SIEM (Security Information and Event Management) systems are now important tools for them. The current study offers a comprehensive analysis of SIEM solutions, such as their key [...] Read more.

Businesses need good defenses against any number of incidents in the continually evolving area of cybersecurity. SIEM (Security Information and Event Management) systems are now important tools for them. The current study offers a comprehensive analysis of SIEM solutions, such as their key features, benefits, installation issues, and suggested procedures. SIEM systems effectively store security event data, giving continuous tracking, interaction, and examination to recognize and deal with threats rapidly. The advantages of this technology include enhanced operating efficiency, streamlined compliance with laws, expedited response to events, and heightened threat detection capabilities. However, the implementation of SIEM systems has many challenges that must be overcome, including intricacies, cognitive exhaustion, data integration complications, and restrictions. To effectively handle these issues, businesses are advised to develop objectives, properly schedule, attend school, and periodically review and enhance their SIEM goals. In addition, organizations may use the complete capabilities of SIEM systems to enhance their cybersecurity stance and mitigate the risks posed by cyberattacks by staying updated with the most recent developments. This study aims to provide a comprehensive examination of Security Information and Event Management (SIEM) systems, with a specific emphasis on important features, benefits, implementation challenges, and suggestions. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

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