Telangana University

The soil and tissue nutrient survey was carried out in 12 blocks representing all the major sugarcane growing soils of the Nizamabad and Kamareddy Districts. As per the critical levels suggested for sugarcane, nearly 80.3, 9.45, 10.02, 25.9, 18.9 and 62.2 per cent of the plant tissue samples are deficient in N, P, K, Cu, Fe and Zn, respectively. Adequate amounts of Mn was noticed in all the sugarcane plant tissues. The data regarding fertility parameters of the sugarcane growing soils nearly 47.24, 91.33, 13.38 and 34.64 per cent of soils are deficient in organic carbon, N, P2O5 and Zn respectively. Adequate amounts of K, Cu, Fe and Mn was noticed in all the sugarcane growing soils. Results on nutrient content of soils and plants, it can be confirmed that the nitrogen deficiency in plants is a reflection of its deficiency in soils. But even though, there is no deficiency of potassium in soils its deficiency in plants is up to 10 per cent. Phosphorus is high in soils and the same is also reflected in plants. Regarding micronutrients, the deficiencies of Zn in soils is also reflected in plants also. But copper deficiency was observed in plants, in spite of the soils were sufficient.

This study investigates work-life balance (WLB) among women employees in the Indian IT sector, focusing specifically on Tata Consultancy Services (TCS), Hyderabad. Drawing on Job Demands-Resources (JD-R) theory, the research examines how organizational factors job nature, workload, work environment, and compensation influence WLB perceptions and outcomes for women professionals facing demanding work cultures and gender role expectations. Primary data were collected from 100 women employees at TCS Hyderabad through a structured questionnaire with validated scales adapted from established literature. The methodology included descriptive statistics for respondent profiling, Pearson correlation analysis for variable relationships, and one-way ANOVA to test hypotheses linking job experience levels (0-5 years, 6-10 years, 11-15 years, >15 years) to WLB factors. Findings indicate that while 78% of respondents report satisfactory overall WLB, challenges remain substantial: 56% experience frequent work-related fatigue, and 49% miss quality personal time due to work pressures. ANOVA confirms work environment significantly affects WLB across experience levels (F=4.12, p<0.05), with mid-career employees (11-15 years) perceiving stronger support than early-career staff. Job nature, workload, and compensation showed no significant experience-based differences, highlighting uniform systemic pressures. Practically, IT organizations should implement flexible hybrid policies, bolster wellness programs with mental health support, expand maternity/childcare benefits, and nurture supportive cultures to enhance retention, productivity, and gender diversity in India's 5+ million-strong IT-BPM workforce. This research offers original firm-specific evidence on post-2020 WLB determinants, filling gaps in hybrid work studies and experience-factor dynamics while extending JD-R theory for HR applications in high-growth tech sectors.

Considering growing environmental concerns and needs for food security, crop protection, and pest control are essential elements of sustainable agriculture.The publicly accessible IP102 dataset, which includes photos of 102 pest species taken in a variety of climatic and environmental settings, is used in this work to demonstrate how machine-learning approaches can be applied to crop picture analysis.An important obstacle to automated pest categorization in this dataset is the variation in illumination, background, and image quality.A thorough preparation procedure is part of our strategy to improve dataset quality and reduce environmental variability.The foundation for classifying pest species is a convolutional neural network (CNN), which allows the model to extract intricate patterns and characteristics from the data.Data augmentation techniques are also used to strengthen the model's resilience and make it more appropriate for actual agricultural situations.This study lays the groundwork for creating machine learning models that can effectively identify pests, assisting researchers and farmers in putting timely and focused pest control measures into place.This work advances precision farming methods and creates avenues for future research to improve agricultural output and sustainability by investigating the potential of combining machine learning with agriculture.

This paper presents a conformal circularly polarized antenna for fifth generation $(5 \mathrm{G})$ Internet of things (IoT) devices. The antenna constitutes a simple configuration employing one pair of mutually perpendicular F -shaped dipole elements. The elements are printed on both sides of a semiflexible substrate layer, connected through delay lines, and exciting the 5 G n78 band. Circular polarization is achieved by the addition of 90° phase shift by the vacant quarterwavelength delay lines. A metallic reflector layer is positioned beneath the antenna to achieve improved performance. The metallic reflector-backed 5 G antenna achieves an impedance bandwidth ($\boldsymbol{S}_{\mathbf{1 1}} \leq-\mathbf{1 0 ~ d B}$) in $\mathbf{3. 2 1 - 3. 7 2 ~ G H z}$ and an axial ratio bandwidth ($\mathrm{AR} \leq 3 \mathrm{~dB}$) in $3.48-3.58 \mathrm{GHz}$, in the 5 G n 78 band. The peak gains increase from 3.95 dB at 3.66 GHz to 7.37 dB at 3.4 GHz, after the addition of the metallic reflector. The conformability, simple design, good bandwidth, polarization, and gain performance make the configuration a suitable candidate for $\mathbf{5 G}$ IoT devices.

This research focuses on utilizing unsupervised anomaly detection methods to uncover irregularities within thyroid-related datasets, specifically employing the “An Thyroid Unsupervised Anomaly” benchmark. Three key algorithms Isolation Forest, One Class SVM, and Local Outlier Factor (LOF) are applied and assessed through precision, recall, and AUC ROC metrics to evaluate their effectiveness in detecting abnormal patterns. The results indicate a significant occurrence of anomalies linked to certain physiological and biochemical parameters, highlighting possible data inconsistencies or deviations in thyroid function measures. Furthermore, the study emphasizes that effective feature selection substantially enhances model precision and anomaly detection capability. By integrating domain specific insights into the analytical process, this work presents a more interpretable and reliable framework for anomaly detection in medical datasets, ultimately supporting more accurate diagnostics and data-driven clinical decision-making in healthcare analytics.

Movie ratings are very important for figuring out how to market films, create better content, and keep audiences happy. In today's world, movies bring people together from all sorts of backgrounds, thanks to streaming platforms and tons of online data. This project dives into the IMDB dataset, which has a lot of information like ratings, genres, cast, and runtime, to study and predict how movies score with viewers. We used machine learning to investigate what makes a movie rating high or low, looking at things such as genre, how long the film is, and other details. By cleaning the data, picking the right features, and building models, we worked to predict ratings as accurately as possible. Our findings, like a slight link between shorter movies and better ratings, can help filmmakers and studios make smarter choices about what movies to make and how to promote them.

The increasing burden of chronic diseases poses major challenges to healthcare systems worldwide, necessitating innovative approaches for timely diagnosis and effective management. This study investigates the transformative role of Artificial Intelligence (AI) in predictive diagnostics for chronic conditions. It focuses on various AI and machine learning algorithms applied to medical data for early detection and prediction of disease onset and progression. The primary objectives include developing robust models capable of generating personalized treatment recommendations, evaluating the impact of AI adoption on improving patient outcomes, and analyzing its potential to reduce healthcare costs. Additionally, this research explores strategies for seamless integration of AI-driven tools into existing clinical workflows and healthcare infrastructures. Ultimately, the study aims to enhance diagnostic precision, optimize therapeutic interventions, and contribute to the advancement of chronic disease management through intelligent, data-driven predictive systems.

Deep learning has rapidly advanced in both performance and development, but the training of deep learning models is often time-consuming and resource intensive, and needing adjustment on a large number of parameters. This research looks at whether or not Bio inspired optimization (GA and PSO), can help deepen deep learning itself, so to speak. By providing these nature inspired methods, the magic of automated model setting, these methods can help you to automatically find the best model setting for you to speed up training, increase accuracy, and reduce overfitting. Through employing GA and PSO on tasks such as hyperparameter tuning and optimization of models are deep learning models made more adaptive, efficient and robust. The techniques are then compared with traditional optimization methods and their use in building better deep learning models with less manual effort is demonstrated.

Stress has become a major health concern in modern society, affecting both physical and mental well-being. Accurate detection and classification of stress levels can play a crucial role in promoting timely interventions and improving quality of life. This research presents a smart stress analysis framework using Support Vector Machines (SVM), a robust machine learning algorithm widely recognized for its effectiveness in handling high-dimensional data. Physiological signals are utilized as primary inputs to capture stress-related variations, which are then processed to classify stress into three levels: low, moderate, and high. By incorporating kernel-based methods, the SVM model effectively identifies complex linear and nonlinear relationships in the data, ensuring reliable separation of stress patterns. The proposed approach demonstrates strong generalization capabilities, making it suitable for real-world applications in healthcare monitoring, workplace wellness, and personalized stress management. Overall, this study emphasizes the potential of SVM in building intelligent systems for efficient stress detection.