Category Archives: Uncategorized

Authors: Research Scholar Arpit Urmaliya, Professor Jitendra Kumar Maitra

Abstract: This paper is intended to create and analyze a novel Color-Aware Adaptive Wavelet Transform (CA-AWT) method for natural color-image denoising with a special focus on the robustness to speckle noise which is realized by adaptive blending and image-based noise estimation. All 68 CBSD68 color images were used in the experiments performed in MATLAB. The images have been converted from RGB to YCbCr and corrupted with six different types of noise (Gaussian, salt-and-pepper, speckle and three mixed-noise types) at three different noise levels (0.05, 0.10 and 0.15), with the result that 1,224 controlled noisy-image experiments were generated. MSE, RMSE, PSNR, SSIM and MAE were used to evaluate CA-AWT baseline V1 and progressive variants V2-V4. The severity dependent blending factors identified from speckle specific ablation analysis encouraged the implementation of V5 continuous noise-estimated adaptation and V6 calibrated nonlinear noise adaptation. Paired t-tests, Wilcoxon signed-rank tests and Cohen's d were used for statistical validation. The results indicated that CA-AWT was effective in most Gaussian and salt-and-pepper noise conditions, but was not effective at low levels of speckle noise, thus necessitating adaptive processing. In the presence of speckle noise, V4 consistently outperformed V1 by 1.277–4.848 dB with large to medium effect sizes. In general, V4 obtained the highest mean PSNR (29.7301 dB) and V6 obtained the highest mean SSIM (0.901595) and the PSNR (29.5587 dB) was also competitive. V6 is thus a potential method of adaptation based on images, but V4 is the present reference method until it is validated in this data set and in the future.

DOI: https://doi.org/10.5281/zenodo.22341269

Published by: vikaspatanker

Authors: Vikrant Diwakar

Abstract: User-generated content (UGC) has emerged as a key element in the digital economy, posing intricate issues about copyright ownership, management, and monetization. This paper investigates the changing legal framework for UGC, highlighting the dynamic interplay among creators, online platforms, and new technologies. Users are generally the initial creators and may be the principal copyright owners of their content, subject to statutory exceptions; however, ownership can be materially affected in practice by platform licensing terms, which may confer extensive permissions on service providers. The study examines moral and economic rights, contractual licensing, rights-management tools such as metadata and Creative Commons licensing, blockchain-based provenance, content removal and archiving, revenue-sharing, artificial intelligence, privacy, publicity and cross-border enforcement. The revised version preserves the original discussion while correcting legal overstatements, especially the description of copyright as a 'Fourteenth Amendment System' and the treatment of non-copyrightable material as automatically public domain. It also adds a stronger Indian legal framework, verified authorities, comparative analysis and a focused research gap. Particular attention is given to AI-assisted and AI-generated UGC, including human authorship, originality, training-data questions and the 2026 Indian framework concerning synthetically generated information. The paper advocates a balanced and adaptive approach to copyright governance that protects creator interests, promotes transparent platform licensing and revenue sharing, supports responsible moderation, and permits technological innovation.

DOI: https://doi.org/10.5281/zenodo.22253782

Published by: vikaspatanker

Authors: Balvir Singh Thakur, Devanshi

Abstract: Regression testing is a vital part of software testing. Executing all test cases from large test suites without considering their historical failure behaviour may lead to time-consuming and inefficient execution. Running the test cases that are more prone to failure first can improve this process. In this paper, an AI-based test case prioritisation approach for the automated testing framework based on TestNG using machine learning and historical test execution data is proposed. The proposed approach extracts execution-based features like total test runs, number of failures, failure rate, average execution time and recent failure information and uses a Random Forest classifier to identify high-risk test cases. The predicted risk information is then used to produce an ordered list of the test suite, ordered by priority. The approach was tested on 70 TestNG test cases with 20 previous executions compared to a normal execution order and an alphabetically ordered static baseline using the Average Percentage of Faults Detected (APFD) metric. The experimental results show that the AI-prioritized execution order results in an APFD of 0.7100, compared to 0.5643 for the normal order and 0.5729 for the static priority order, improvements of 25.82% and 23.94%, respectively. The results show that taking into account historical failure behaviour in machine learning-based test prioritisation can improve the early detection of failing test cases and provide a more risk-aware execution strategy for regression testing.

DOI: https://doi.org/10.5281/zenodo.22252547

Published by: vikaspatanker

Authors: Professor Priyanka Mahajan, Rohit Balaji Pingale, Vaishnavi Laxman Shinde

Abstract: A India's electronic waste (e-waste) generation has increased rapidly, while formal collection and recycling infrastructure remains concentrated in major metropolitan areas. Tier-2 and Tier-3 cities continue to face challenges in planning efficient collection networks due to limited data-driven decision support. This paper presents a machine learning-based framework for estimating ward-level e-waste generation using publicly available demographic, economic, and administrative indicators. A Random Forest regressor is proposed as the primary prediction model and is benchmarked against Gradient Boosting and Ordinary Least Squares regression to evaluate predictive performance. The framework incorporates a reproducible data-engineering pipeline integrating data from the Central Pollution Control Board (CPCB), State Pollution Control Boards (SPCBs), Census records, and Urban Local Bodies (ULBs). To validate the proposed workflow before large-scale field deployment, a clearly labelled synthetic dataset is developed for model training and evaluation. Feature importance analysis is further employed to identify the key factors influencing e-waste generation, enabling practical insights for municipal authorities and Extended Producer Responsibility (EPR) stakeholders. The proposed approach offers a scalable, low-cost, and replicable methodology for supporting sustainable e-waste management, optimizing collection planning, and reducing the infrastructure gap between metropolitan and emerging urban regions in India.

DOI: https://doi.org/10.5281/zenodo.22252069

Published by: vikaspatanker

Authors: Komal, Balvir Singh Thakur

Abstract: A major challenge for the telecommunication industry is the frequent customer switching, resulting in substantial revenue loss and increased customer acquisition costs. Most of the existing studies on churn prediction focus on the predictive accuracy and pay less attention to the interpretability of the model and how to convert the model output to business-facing visual tools. In this study, an integrated framework is proposed with XGBoost for churn classification, SHAP (Shapley Additive explanations) for global and local interpretability and Tableau for the interactive dashboard visualization. The Telco Customer Churn dataset with 7,043 customer records and 21 features is publicly available and is used for experimentation. The accuracy of the XGBoost model on a held-out test set of 1,409 customers is 80.41%, with better results on the majority (retained) class than on the minority (churned) class, which is consistent with the moderate imbalance between classes in the dataset. This outcome will be contextualized through baseline comparison against Logistic Regression and Random Forest. SHAP analysis reveals that contract type, tenure, monthly charges, total charges and internet service type are the dominant churn drivers globally, and local SHAP explanations illustrate how these factors combine for individual customers. In addition to the SHAP-ranked churn drivers, an interactive Tableau dashboard was created to visualize churn distribution by contract type, payment method, and internet service type for business-facing exploration of these insights. The study concludes that the combination of predictive accuracy with explainability and visualization leads to a more actionable churn-management framework than accuracy-oriented approaches alone.

DOI: https://doi.org/10.5281/zenodo.22245020

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Kalyani Shantanu Kulkarni
Affiliation	Assistant Professor, Department of Computer Science G. H. Raisoni Skill Tech University (GHRSTU),Nagpur.
Email-Id:	Kalyani.kulkarni@ghrstu.edu.in
Publication: Patent Kalyani S. Kulkarni , AI Integrated Software Development Device , application no. 509396001 , 2026. Books:  Prof. Kalyani S. Kulkarni,Mastering the Art of Software Design: Key Principles for Scalable Systems  Chyren Publication 2025. Publications: Explainable AI-Enavled Computer Vision For Human Activity Recognition In Smart Surveillance , A Critical Review 2026. K. Ulabhaje and S. More., 2018. Survey on image fusion techniques used in remote sensing. International Journal of Advance Research, Ideas and Innovations in Technology (IJARIIT), Vol. 4, Issue 5. S. More and K. Ulabhaje., 2018. A Review of image change detection algorithms for multi-temporal data. Journal of Emerging Technologies and Innovative Research (JETIR), Volume 5, Issue 10. K. Ulabhaje and G. Karemore., 2018. Path-breaking role of big data and learning analytics in education: A survey. International Journal of Engineering Sciences and Research and Technology (IJESRT), Vol. 7, Issue 8. R. Neware and K. Ulabhaje., 2018. Survey on issues in mobile cloud computing and its prevention measures. International Conference on Advanced Computing, Networking and Informatics (ICACNI-2018).

Published by: vikaspatanker

Authors: Zachariah Munyoro Mwangi, Alice Nambiro

Abstract: Beach-litter management involves taking measures to minimize waste accumulation along the coastal environments towards healthy marine ecosystem. It involves various strategies including anthropogenic waste prevention, monitoring, mitigation and removal. Installation and positioning of trash bins, and interactive signage is one of the basic mechanism that plays key role in eradication of beach litter. Beach-litter management also entails environmental awareness, targeted campaigns and other behavioural interventions to reduce the gap between beach users’ actual behavioural characteristics and standard environmental values. However, evidence on specific paired behavioural relationships among Kenyan coastal beach users remains limited. This study examined four relationships between bin visibility versus bin use, disposal guidance versus litter-disposal behaviour, noticing litter versus concern about litter and frequency of beach visits versus litter-disposal behaviour. Cross-sectional questionnaire survey was conducted on selected beaches in Mombasa, Kilifi and Kwale involving 287 valid respondents. Responses were categorised, cross-tabulation and Pearson’s chi-square was determined. Simulated p-values were undertaken where necessary with Cramer’s V for association magnitude determined. There was statistical significance between the first three relationships, that is, bin visibility versus bin use, which had the largest observed association magnitude of 39.4%, disposal guidance versus litter-disposal behaviour and noticing litter versus concern about litter. The fourth relationship, frequency of beach visits versus litter-disposal behaviour was not statistically significant. This suggests that visiting beach many times does not necessarily influence change in beach user’s behaviour towards littering

DOI: https://doi.org/10.5281/zenodo.22207685

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Authors: Abirami A, Miranda Lakshmi T, Martin A

Abstract: Conversational emotion recognition is inherently challenging because an utterance's emotional label does not depend on wording alone — it is shaped jointly by content, delivery, and the dialogue history surrounding it. This work introduces a multimodal deep-learning pipeline that fuses textual, acoustic, and contextual signals to classify emotion at the utterance level within conversations. Linguistic meaning is captured through pretrained BERT embeddings, broad acoustic patterns through Wav2Vec2 embeddings, and explicit vocal-affect cues through handcrafted prosodic descriptors (pitch statistics and short-time energy). Once concatenated at the utterance level, these heterogeneous features are passed into a Bidirectional Gated Recurrent Unit (BiGRU) that captures dependencies across dialogue turns, with a fully connected head performing the final classification. Because the benchmark dataset's seven-class emotion taxonomy is heavily skewed, a weighted cross-entropy loss built from inverse class-frequency statistics is applied to offset this imbalance during training. On the MELD (Multimodal EmotionLines Dataset) benchmark, the proposed pipeline reaches 77.56% overall accuracy and a weighted F1-score of 0.804, with per-class ROC-AUC spanning 0.89 to 0.96. Confusion-matrix and error analysis reveal that common emotions such as neutral and joy are classified reliably, whereas rarer emotions such as fear and disgust remain substantially harder to recognize even with class-weighted training — underlining how persistent the data-imbalance problem remains in this domain and pointing toward future work on stronger imbalance-handling and context-modelling techniques.

DOI: https://doi.org/10.5281/zenodo.22204728

Published by: vikaspatanker

Authors: T. Gopalakrishnan, Boopathi S, B. Niranjan, Mukesh P.M

Abstract: Global almond (Prunus dulcis) production leaves behind a shell fraction that exceeds the mass of the kernel it protects, and the great majority of this lignocellulosic by-product is presently burned, landfilled or sold at low value as biomass fuel. Because the shell is hard, largely cellulosic and available in very large and geographically concentrated volumes wherever almonds are processed, it is a natural candidate for the same role that walnut shell, groundnut shell, coconut shell and rice husk already occupy in the particulate-filled polymer composite literature: a low-cost, renewable, low-density filler for thermosetting resins. This review draws together what is presently known, and what is not yet known, about almond shell powder as a reinforcement for epoxy matrices. It begins with the scale and composition of the waste stream, since both bear directly on whether the material is a viable industrial filler rather than a laboratory curiosity. Processing considerations — drying, size reduction, sieving and surface treatment — are then examined, followed by the mechanical, physical, water-absorption, thermal and morphological behaviour reported for shell-powder-filled epoxy systems, read against the much larger body of work on chemically similar agro-waste fillers where almond-specific data are still sparse. The review closes by identifying the almond shell literature as considerably thinner than that for walnut or coconut shell despite comparable material properties, and names surface treatment optimisation, hybridisation with synthetic or other natural reinforcements, durability under moisture and weathering, and a proper life-cycle assessment against synthetic fillers as the most pressing gaps.

DOI: https://doi.org/10.5281/zenodo.22203936

Published by: vikaspatanker

Authors: Zachariah Munyoro, Alice Nambiro

Abstract: Different researchers have carried out Beach-litter prediction, with basis of their studies being detection and counting of existing litter. Forecasting requires integration of temporal litter patterns, with contextual conditions that influence accumulation. Continuous monitoring of beach litter is necessary within this area of study. Conventional process of monitoring provides observation to a given extent, which limits rapid or automated assessment. AI-based detection allows automatic identification and quantification, allowing monitoring to continue from detection towards prediction. Existing methods address detection, forecasting or contextual integration separately. Various predictive studies use historical litter records, contextual variables, camera observations and Machine Learning. Limited architectural integration connects AI-derived litter measurements directly with temporal and contextual information for multi-predictor variables within multi-duration dimensions. This study developed an AI-based predictive model architecture. The model connected automated litter measurement temporal construction, contextual integration, predictive modelling and forecasting generation. Images were captured through IoT-enabled cameras, subjected under RT-DETRv2, which detected, classified and counted the objects. The output of this process is category-specific quantitative litter observations. The image-level counts are aggregated into beach-week observations, forming temporal litter representation. The temporal information provides the predictive baseline. The predictive model generated category-specific forecasts at multiple future durations. These were weekly, bi-weekly and monthly forecasts. The architecture linked AI-based measurement (RT-DETRv2), data integration, prediction and monitoring output in one end-to-end flow. The model provides a framework for anticipatory beach-litter monitoring instead of detection alone. The output of the predictive model architecture provide diverse options necessary for specific management decision making as well as a feedback loop to the predictive process.

DOI: https://doi.org/10.5281/zenodo.22303136

Published by: vikaspatanker