Sentiment analysis of customer reviews is an important tool in getting to know customer opinion, quality of service and product feedback. Nevertheless, classic approaches tend to label a whole review set of coarse polarity categories, which does not reflect aspect-based opinions and latent emotional trends. In order to address this deficiency, this paper proposes a new Emotion-Guided Aspect-Aware Sentiment Classification System which can effectively and reliably classify user reviews. The proposed architecture integrates the following processes: contextual preprocessing, aspect extraction, fine-grained emotion detection, memory of emotion prototype, retrieval-enhanced reasoning, emotion-sensitive feature fusion, sentiment classification and confidence-based filtering, into one architecture. The system can identify the key aspects of quality, delivery, as well as support and then emotions such as joy, anger, trust, frustration, sadness, and disappointment. These affective signals has employed in order to enhance the sentiment forecasting. Experimental data show that the proposed model reaches 93.40 percent accuracy, 92.85 percent precision, 92.30 percent recall, and 92.57 percent F1-score that are better than the base models like SVM, LSTM and BERT. The ablation experiment also verified that all the elements associated with aspect extraction, emotion detection, prototype memory, retrieval module and confidence filtering are all factors contributing to the boost in performance.
Vallem Sushma Latha, Shanker Chandre, Erukala Sudarshan· ITM Web of Conferences· 0 citations
Experiments indicate that HHBA-GA achieves the best performance in makespan, energy consumption, and cost efficiency compared with GA, PSO, and standalone HHBA, and the model significantly enhances resource usage, proving its effectiveness in large-scale edge-cloud applications.
P. Thai, S. Chandre· Proceedings of the 1st Inter...· 0 citations
Cloud Computing (CC) is the cornerstone of modern information technology that provides scalable, flexible, and cost-efficient services across diverse applications. Dynamic workloads and heterogeneous infrastructure face some difficulties in effective load balancing and resource provisioning which results in resource underutilization, overload and response time increases. This paper presents a comprehensive and comparative analysis of current methods addressing these issues. It also presents active resource provisioning frameworks, namely: probabilistic load balancing models, Machine Learning (ML)-based, Deep Learning (DL)-based, workload prediction techniques, genetic algorithms, Reinforcement Learning (RL) strategies, and hybrid meta-heuristic methods. Each method is analyzed in terms of methodology, advantages, limitations, and performance metrics, therefore providing an insight of their applicability in dynamic and large-scale cloud environments. A taxonomy architecture is presented to categorize the systematic comparison and research gaps. The comparative evaluation segment demonstrates enhancement in throughput, resource utilization, and cost efficiency, while also identifying limitations such as computational overhead and scalability constraints. The survey concludes by highlighting the necessity for intelligent, adaptive, and energy-aware solutions to confirm resilient and efficient cloud infrastructures.
Prasanna Mandala, S. Chandre· 2026 5th International Confe...· 0 citations
: Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud faces challenges of high-dimensional state spaces and scalability with increasing tasks. To solve this problem, the Masterpiece Optimization Algorithm (MOA) with a priority constraint is employed for load-balancing according to the tasks efficiently. The MOA is integrated with a priority-based cost function to enhance the task scheduling process by introducing a multi-dimensional approach for load balancing. The priority-based framework helps the scheduler to dynamically recalibrate workloads. The experimental results achieve a total energy consumption of 39.8 W and an average CPU resource utilization of 99.54%, which is better than the existing algorithms, such as the hybrid Particle Swarm Grey Wolf Optimization (PSGWO) algorithm.
S. Vijaykumar, S. Chandre· Journal of Computer Science· 0 citations
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