Authors - Arpita Choudhury, Pinki Roy, Sivaji Bandyopadhyay Abstract - Modern agriculture faces several challenges such as uncertain crop selection, inefficient fertilizer usage, and changing soil conditions. To address these issues, this research proposes an integrated AI/MLbased system that combines crop recommendation, fertilizer recommendation, and time-series prediction. The system utilizes IoT sensor data, including soil nutrients (N, P, K) and environmental parameters such as temperature and humidity, to support data-driven decision-making. Random Forest models are used for crop and fertilizer recommendation, while an LSTM-based model is applied for predicting future soil conditions using time-series data. Basic preprocessing techniques are used to ensure data quality, and results are presented through a simple and user-friendly dashboard. Experimental results demonstrate strong performance, with 96% accuracy for crop recommendation and reliable prediction trends for time-series forecasting. Designed for offline use with minimal computational requirements, the system is suitable for deployment in rural and resource-constrained environments, highlighting the practical role of AI/ML in modern precision agriculture.
Authors - Sharayu Mirasdar, Mangesh Bedekar Abstract - Ayurveda, India's ancient system of medicine, is full of inter-connected knowledge about diseases, their symptoms, herb and formulation (compounds). However, texts such as Charaka Samhita are mostly unstructured and cannot be readily analysed computationally. This work presents AyurKOSH which is a machine-readable, high-quality Ayurvedic dataset that is designed as a Knowledge Graph (KG) in order to support Artificial Intelligence driven research. The dataset is represented as subject–predicate–object triplets, which enables semantic interoperability, graph traversal, and multi-hop inferencing across entities. The dataset is designed by following schema-driven ontology which standardizes relationships between various nodes such as diseases, symptoms, pharmacological attributes, and compound formulations. DB Schema ensures consistency and computational tractability. AyurKOSH has the structured data of diseases and related symptoms, drug preparations, herbs and the detailed pharmacological properties are Rasa, Guna, Virya, Vipaka, Karma. The graph structure shows real-world biomedical network characteristics such as high sparsity and low average degree, which makes it suitable for embedding-based learning, graph neural networks, and explainable AI frameworks. Moreover, there is botanical metadata and herb-substitution relationships added for the prediction of synergy and repurposing of drugs. The dataset facilitates applications in biomedical NLP, and automated reasoning systems and clinical decision assistance, and pedagogy in integrative medicine. AyurKOSH became available for academic and non-commercial research under CC BY-NC-SA 4.0 license.
Authors - Liz Huancapaza Hilasaca, Maria Cristina Ferreira de Oliveira, Rosane Minghim Abstract - The abstract of the study emphasizes the thorough discussion of cussword usage in Hollywood films over a period of thirty five years, from 1990 to 2025, particularly in genres such as Action, Comedies, and Romances. On the basis of a carefully selected dataset of cusswords from Kaggle along with a considerable subtitle file dataset (.srt), the results have been obtained to determine whether profanity has been used over the years with an appropriate level of intensity in the respective genres of films.
Authors - Lanja Azeez Abdalqadir, Aram Mahmood Ahmed, Rozha Kamal Ahmed, Dirk Draheim Abstract - This study explores advanced metaheuristic optimization algorithms to improve smart home energy management under constrained electricity supply, aiming to reduce costs and enhance energy efficiency. It addresses challenges such as dynamic pricing and unstable supply, particularly common in developing regions. Five algorithms—Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Fitness Dependent Optimization (FDO), Marine Predators Algorithm (MPA), and Single Candidate Optimization (SCO)—are evaluated, along with enhanced versions of MPA, FDO, and SCO incorporating Lévy flight and Oppo-sition-Based Learning (OBL). OBL improves exploration and exploitation in FDO and MPA, while Lévy flight enhances SCO’s ability to escape local optima. A novel cyclic rebounding technique is introduced to manage appliance sched-ules exceeding 24-hour limits. Tested across three scheduling scenarios, results show that MPA-OBL consistently achieves the lowest energy costs. Overall, the proposed enhancements significantly improve energy optimization in supply-constrained environments.
Authors - Purva Trivedi, Arun Parakh, Shurbhit Surage Abstract - Awareness regarding consumer sentiments will benefit a business en tity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing sce narios, since there is a lack of actual emotional aspect in expressing views in real time contexts, it has always been challenging for a business to perform a signifi cant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analy sis in digital marketing. The framework is made up of seven core steps: data collection, preprocessing and data cleaning, sentiment analysis models, feature extraction and model train ing, sentiment classification and analysis, insights and decision-making, and ap plication in digital marketing. From social media to e-commerce reviews to online discussions, consumer sentiment data comes from many digital sources. The text for analysis is standardized, and noise is cleaned in data prepara tion. Then, apart from other artificial intelligence-based sentiment classification models, sentiments are classified as positive, negative, or neutral using lexicon based, machine learning, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment, target advertise ments, and adjust marketing strategies. Businesses will be able to conduct more profitable promotions, communicate with customers better, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing, even in ad dressing issues like ambiguous sentiment expression management and multi-lan guage data. This paper provides a strategic way towards maximum customer in teraction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.
Authors - Soumyadeep Basak, Shubham Sahu, Sankur Kundu, Ankita Ray Chowdhury Abstract - Hyperspectral image (HSI) classification requires effective modeling of high-dimensional spectral signatures and fine-grained spa tial structures while maintaining computational efficiency for real-world deployment. Although recent Transformer- and state-space-based ap proaches enhance long-range dependency modeling, they often introduce substantial architectural complexity and computational overhead. To ad dress these challenges, we propose MF-HSINet, a lightweight dual branch framework that enables adaptive spectral–spatial fusion via se lective state-space modeling. The spectral branch captures inter-band de pendencies, the spatial branch extracts local structural patterns, and the proposed Mamba-Enhanced Attention Fusion (MAF) module integrates these complementary representations through selective state updates, cross-attention, and adaptive gating to achieve pixel-wise feature balanc ing. This design preserves discriminative local details while strengthen ing global contextual modeling with reduced parameter complexity. Ex tensive experiments on nine benchmark hyperspectral datasets demon strate that MF-HSINet achieves competitive and consistent performance in terms of Overall Accuracy, Average Accuracy, and Kappa coefficient, while offering improved efficiency and inference speed, making it suitable for practical and resource-constrained HSI applications.
Authors - N. Revathy, Tamilmani M, Naveena P, Mariya Nisha S, Mega varshini V, Karthik B Abstract - Virtual Learning Environments (VLEs) are commonly evaluated through expert-driven frameworks that lack reproducibility and objective prioritization of defining features. This study proposes a data-driven framework integrating a Systematic Literature Review (SLR) and the iKeyCriteria method to identify and logically classify core VLE characteristics. A corpus of peer-re-viewed studies was analyzed and divided into VLE-focused (P) and contrastive non-VLE (Q) contexts. Criteria extraction and validation were conducted using tfidf (Term Frequency-Inverse Document Frequency) weighting and Boolean logical matrices to determine necessary and sufficient conditions. Results indicate that structured delivery of learning materials (91.5% in P vs. 12.7% in Q) and shared collaborative workspaces (82.1% vs. 18.2%) function as sufficient but not necessary discriminators of VLEs. In contrast, self-assessment and summative assessment appear frequently across both contexts and are therefore non-distinctive. The proposed framework provides a reproducible and bias-reduced mechanism for distinguishing defining VLE features, bridging systematic review methodologies with logical condition analysis. These findings support evidence-based prioritization in the design and evaluation of digital learning systems and contribute to advancing objective classification approaches in educational technology research.
Authors - Tegawende Brigitte KIENTEGA, Sadouanouan MALO Abstract - Navigation of mobile robots using GPS is widely available but use of GPS is sometimes either costly, not suitable for security reason, not available in indoor environments, or underground operational fields. This work provides a greedy method of path planning for a mobile robot from a starting point to the given destination point in a GPS-denied field where a set of access points (AP) are deployed randomly. Using these APs, the robot is able to calculate its current position at any moment as well as it chooses the next position to move further towards the destination. An efficient algorithm is designed to guide the robot to reach to its destination successfully taking into account that all the holes are convex, if exists within the field of interest. An analysis of the deployment strategy of the APs is provided in order to guarantee the successful path planning by the robot without backtracking any sub path.
Authors - Ambati Abhinavya, Jarupula Sunitha, Raparthy Navya, Rama Valupadasu Abstract - Internet of Things (IoT) devices are growing in domains because of their reliability and efficiency in monitoring, real-time detection and automated support. However, these IoT systems have also introduced security challenges. These devices are vulnerable to cyber threats, where attackers exploit weak points in the system to steal sensitive information. One of the attacks is the Distributed Denial of Service (DDoS) attack, which disrupts services by overwhelming systems and making them inaccessible to legitimate users. IoT devices are resource-constrained, so reducing feature dimensionality is essential to lower computational overhead and complexity. IoT devices generate data for detecting cyber-attacks, but sharing such data across organizations raises privacy concerns. To address these challenges, the proposed approach is designed in two phases. In the first phase, a hybrid feature selection technique using mutual information, permutation feature importance, and Greedy wrapper-based feature selection with cross-validation is applied to extract relevant features. In the second phase, Federated Learning (FL) is applied to train the model without sharing raw data among clients. Within the FL framework, Random Forest (RF) algorithm is utilized for training due to its robustness and classification capability. The proposed model is evaluated under two data distribution scenarios: mildly non-IID and strongly non-IID conditions. Experimental results demonstrate that the model achieved an accuracy of 99.69% in a mildly non-IID scenario and 98.36% under strongly non-IID conditions, highlighting the effectiveness and reliability of the proposed framework for secure IoT-based DDoS attack detection.
Authors - Kalidasu Lochani Krishna Priya, Nupur Ajit Kale, Apeksha Pandurang Mujumale, Anagha Vijaysinha Rajput Abstract - The large online data consist of duplication and plagiarized contents. Due to Artificial Intelligence, data generation has become very easy. But, it may also lack an ethical data generation process. Hence, there is a need of validating plagiarism free data for authentic usage. In this research work, authors focus on word-level plagiarism detection methods in Natural Language Processing. The proposed method uses a comparative analysis of cosine similarity, Euclidean distance and Manhattan distance methods for word-level plagiarism detection for different n-gram sizes. The inculcation of n-gram size improved the accuracy compared to unigram based methods. The experimental results of the cosine similarity method outperform Euclidean and Manhattan distance methods by achieving an average accuracy range of 88 % to 92 % and 75 % to 80 % for direct plagiarism and lightly paraphrased text respectively. The future work is to identify reused images and visual contents.
