Daily Archives: April 9, 2026

Authors: Dr. C. Saravanabhavan, Akhil R

Abstract: Recent advances in deep learning have en-abled highly realistic synthetic speech, creating serious risks such as impersonation, fraud, and misuse of voice-based authentication systems. Detecting AI-generated speech is increasingly difficult because modern text-to-speech and voice conversion models can closely imitate human prosody and timbre across languages. This paper proposes VoiceGuard, a hybrid deep learning framework that combines complementary spectral and temporal rep-resentations for deepfake voice detection. A Convolutional Neural Network (CNN) branch learns frequency-domain artifacts from spectrograms, while a CNN-GRU branch models temporal inconsistencies from acoustic descriptors. An attention-based fusion mechanism adaptively weights branch outputs to improve discriminative power. The framework is evaluated on benchmark datasets and cross-lingual settings, and it improves performance compared to single-representation approaches while remaining compu-tationally practical for real-world deployment.

DOI: https://doi.org/10.5281/zenodo.19480877

 

Published by: vikaspatanker

Authors: Durgunala Ranjith, K.Hari Krishna, K.Rajender, R.Koti

Abstract: The project proposes an AI Startup Idea Validator that helps users evaluate startup ideas automatically using Artificial Intelligence and Large Language Models (LLMs). The system allows users to input their startup ideas through a web interface and analyzes them by considering factors such as market potential, competition, feasibility, and innovation. It uses AI-based processing to generate outputs including feasibility score, SWOT analysis, and improvement suggestions, providing users with clear insights into the strengths and weaknesses of their ideas. The system integrates external data sources and intelligent models to ensure accurate and data-driven decision-making. It is designed to be fast, cost-effective, and user- friendly, making it suitable for students, entrepreneurs, and startup incubators.

DOI: https://doi.org/10.5281/zenodo.19553722

 

Published by: vikaspatanker

Authors: Ambuj Kumar Misra

Abstract: The global agricultural sector faces unprecedented challenges from climate variability, resource depletion, and a rapidly growing population that demands consistent food security. This study presents the design, implementation, and evaluation of an Intelligent Agricultural Advisory System (IAAS) that leverages secondary crop datasets, multi-source meteorological records, and machine learning algorithms to deliver actionable, site-specific farming recommendations. Drawing on publicly available repositories including the USDA National Agricultural Statistics Service (NASS), NOAA Global Historical Climatology Network, and the FAO FAOSTAT database, our framework integrates data preprocessing pipelines, feature engineering modules, and ensemble predictive models comprising Random Forest classifiers and Long Short-Term Memory (LSTM) networks. Field validation across five Midwestern U.S. counties over a three-year period (2020-2023) demonstrated an average crop yield prediction accuracy of 91.4%, a 23.6% improvement in farmer decision-making efficiency, and a measurable reduction in water usage compared to conventional irrigation scheduling. The system's modular architecture supports deployment across a web dashboard and a mobile application accessible to smallholder and commercial farms alike. Our findings confirm that intelligent advisory systems built on secondary data are both technically feasible and economically significant, offering a scalable pathway toward precision agriculture for diverse agro-climatic regions.

DOI: https://doi.org/10.5281/zenodo.19479446

 

Published by: vikaspatanker

Authors: Ganesh Racha

Abstract: Financial technology is advancing rapidly, especially now since standard credit scoring methods are becoming obsolete. With scoring methods being archaic and out of touch, countless valuable behavioral data are not captured. In this study, the author discusses how possible behavioral data can be found in financial and transaction data using an AI-powered financial insight engine. It aims to change the predict and prescriptive analytics to enhance the better credit decision processes, beyond the usual finance means. Rather than referring to historical financial data and comparing it, behavioral data that is not ordinary are looked into particularly in expenditure. The result is a changing credit score that is indicative of the dynamic character of credit management. The use of advanced machine learning methods like Random Forest, Neural Networks and Gradient Boosting are remarkable in evaluating the above standard behavioral data and relationships, which are usually deemed to be irrelevant. The experimental results show that these models compared with other traditional methods like Logistic Regression are more accurate, precise and has better recall and score f1. In addition, the analysis of spending behavior has been integrated to introduce common financial user behavioral patterns and improve risk assessment and measurement of financial stability. An improved system demo is integrated that use cases widely for companies and banks. To similar how the companies were formed with tech such as Netflix, Samsung, Google and Uber changing the algorithm of credit check by enhancing AI algorithms along with blockchain records based validation, are used to analyse paticipants in this open eco-system.

DOI: https://doi.org/10.5281/zenodo.19479408

Published by: vikaspatanker

Authors: Ambuj Kumar Misra

Abstract: Accurate crop yield prediction is essential for food security, agricultural planning, and policy formulation. This research paper presents a comprehensive analysis of district-level crop yield prediction using government open data and artificial intelligence techniques [1]. The study leverages publicly available datasets from agricultural ministries, meteorological agencies, and remote sensing sources to develop predictive models utilizing machine learning and deep learning approaches. Our analysis demonstrates that ensemble methods combining multiple algorithms achieve superior accuracy compared to individual models, with R² values exceeding 0.85 on validation datasets [2]. The proposed framework integrates soil characteristics, weather patterns, crop management practices, and historical yield data to create robust prediction systems deployable across different geographical regions [3]. Results indicate that incorporating remote sensing data and temporal patterns significantly improves model performance [4]. This research contributes to the growing body of knowledge on precision agriculture and provides practical guidelines for government agencies and farmers to optimize yield forecasting systems.

DOI: https://doi.org/10.5281/zenodo.19479317

 

Published by: vikaspatanker

Authors: Dr. Ekata Singh

Abstract: Organic synthesis uses solvents in nearly every stage of a chemical process. Solvents are very important as they dissolve reactants, enhance mixing, control reaction temperature and make it easier to isolate and purify the final product. Solvents also affect the speed and result of the reaction, in many cases. Solvents are widely used in laboratory research and industrial chemical production. But many of the most commonly used solvents are dangerous. They are toxic, flammable, highly volatile and difficult to dispose of safely. Common examples include benzene, chloroform, dichloromethane, N,N-dimethylformamide (DMF) and N-methyl-2-pyrrolidone (NMP). Long-term or repeated exposure to such solvents may affect skin, lungs, liver, nervous system and in some cases even increase the risk of cancer or reproductive damage. If not managed properly, solvent waste may pollute air, water and soil. As a result of these concerns, chemists are now giving more emphasis to reducing the use of hazardous solvents in organic synthesis. This is one of the central goals of green chemistry. Green chemistry promotes the designing of safer chemical processes that use fewer harmful substances and create less waste. This paper explains why hazardous solvents are widely used in organic synthesis and how they lead to health, safety, and environmental hazards. It also discusses the main green chemistry methods used for the reduction of solvent-related hazards. And this is where we want to see if we can replace hazardous solvents with safer ones, use greener solvents, carry out solvent-free reactions, design better reaction structures and recover or recycle solvents after use when we want to. Some good things have been done so far but a lot of scientific and practical problems remain. Still, the right way forward is to reduce the use of hazardous solvents so organic synthesis is safer and cleaner.

DOI: https://doi.org/10.5281/zenodo.19479101

Published by: vikaspatanker

Authors: Dr. Ekata Singh

Abstract: In the environment and the world of heavy metals discharge into water bodies is still a serious environmental and public health issue, especially in areas of heavy industrial activity. Electroplating, metal finishing, mining, tannery, battery, textile and pigment industry wastewater has toxic levels of lead, cadmium, chromium, nickel, copper, zinc, arsenic, and mercury. These metals are not biodegradable and are commonly deposited in sediments, biota and food chains, and hence pose a greater environmental risk as well as human risk. Adoption of adsorption is one of the most effective ways to remove heavy metals, and the low concentrations, low cost and economic feasibility of adsorption make it the current best treatment method. The heavy metals are present in industrial effluents, and adsorption can be considered as a treatment strategy with lower cost and renewable adsorbents. The performance of activated carbon, natural minerals, agricultural by-products, biosorbents and new nanostructured materials is evaluated in terms of adsorption capacity, removal mechanism, regeneration and real-world applications. pH, contact time, adsorbent dose, competing ions and surface chemistry are also addressed. There is solid evidence that many of the low-cost adsorbents are capable of metal uptake in laboratory settings, but still, a huge amount of work remains on scaling, regeneration efficiency, stability in complex effluents, and disposal of spent sorbents. A great deal of progress in the future will be to compare results, to have realistic wastewater testing, and to integrate adsorption into circular and resource recovery-based treatment processes.

DOI: https://doi.org/10.5281/zenodo.19478874

Published by: vikaspatanker