Human Resource & Management (HRM) plays a vital role in organizational operations. The HRM tries to produce optimal output from human resources through workload balance. One of the core factors of workload balance is stress management. Although Deep Learning technology has introduced revolutionary applications in different sectors, its application in HRM is still nominal. This paper proposes an innovative application of Deep Learning to classify stressed and satisfied employees automatically. This generalized adaptive method utilizes quantitative measures which ensure unbiased classification with 88.40% accuracy and 0.8728 F1-score. The proposed network outperforms similar approaches, paving the path to applying Deep Learning based solutions to ensure a better workplace and proper workload balance through an effortless automatic but reliable stress classifier.

The legal industry struggles with inefficiencies, high costs, and manual-intensive workflows. Traditional AI lacks adaptability in optimizing legal operations. To address this, we propose LegalMind, an agentic AI-driven framework leveraging DeepSeek R1 for intelligent legal process automation and cost reduction. LegalMind integrates a structured legal dataset and fine-tunes DeepSeek R1 to enhance decision-making and workflow efficiency. Experimental results show a 42.6% cost reduction and a 60.8% improvement in document processing speed over baseline AI models. Scalability tests confirm the system’s ability to handle 100,000 queries efficiently. Real-world case studies validate LegalMind’s effectiveness in law firms, corporate legal departments, and government agencies, demonstrating significant reductions in case preparation time and operational costs. These findings highlight the transformative potential of agentic AI in legal automation, optimizing workflows and improving decision support.

Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, a Generative AI-based framework designed to analyze and optimize business costs through intelligent decision support. The framework employs a transformer-based model with over 140 million parameters, fine-tuned using a diverse dataset of cost-related business scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates cost analysis tasks, and provides actionable insights to decision-makers. The proposed framework achieves exceptional performance metrics, including a prediction accuracy of 93.2%, precision of 93.5%, recall of 93.1%, and an F1-score of 93.3%. The perplexity score of 20.17 demonstrates the model’s superior language understanding and generative abilities. Gen-Optimizer was tested in real-world scenarios, demonstrating its ability to reduce operational costs by 4.11% across key business functions. Furthermore, it aligns with sustainability objectives, promoting resource efficiency and reducing waste. This paper highlights the transformative potential of Generative AI in business cost management, paving the way for scalable, intelligent, and cost-effective solutions.

No two humans are identical. There are variations in their capability, thinking process, and personality. That is why human society is diverse. This diversity is visible everywhere, including in office environments. The office environments are diverse based on culture, service, and goals. Offices, where the workforce uses computing devices to perform their responsibilities, are mostly sedentary settings where employees must remain seated during office hours. Perseverance and self-motivation are mandatory to make sedentary office hours effective. However, these qualities are not common to everyone. It causes an imbalance in workload distribution and scheduling which facilitates the possibility of irrelevant performance evaluation. This paper addresses these issues and proposes a novel algorithm to optimally schedule the workforce in a sedentary office environment. The proposed algorithm uses 2 ^nd order polynomial kernel-based Support Vector Machines (SVM) classifier and classifies human activities with 95.0% accuracy. This accurate classification is further utilized to optimally schedule the workforce, which improves performance by 30.6% and saves an average of 1 hour and 2 minutes per day.