Mr. Rajesh Tirupathi

This work is a comparative study of the traditional fuzzy goal programming model and the chance constrained fuzzy goal programming model. The right-hand side coefficient of the constraint matrix is assumed to be a right sided fuzzy number, and a random variable following the gumbel distribution, while the coefficients of the constraint matrix are triangular fuzzy numbers.The chance constrained problem is converted to its deterministic equivalence and the fuzzy constraints defuzzied. The bounds of the kth objectives are determined and utilized to obtain the membership function of the fuzzy goals. Lastly, the weighted sum goal programming technique is employed to obtain the optimal solution to the decision makers goal target using the attained membership function. Moreover, the CCFGP model proved a more satiscing approach to optimize the decision makers goals as it yielded an under-achievement of the decision makers goal target. Numerical illustration proved the superiority of the technique.

Object detection in low-light conditions presents significant challenges due to the reduced visibility and poor illumination, particularly in real-time applications. This paper proposes a novel approach using the YOLOv8 model for real-time object detection in night-time conditions. A custom dataset comprising various objects captured in low-light environments was utilized to train and evaluate the model. The results demonstrate superior performance in terms of speed and accuracy compared to previous models, particularly YOLOv3. We also include an analysis of the model's real-time performance using a custom video feed. Our findings show that YOLOv8 outperforms earlier YOLO versions in detecting objects accurately and quickly in low-light, real-time scenarios, making it a promising solution for night-time surveillance and other security-related applications.

Objective: Anthropomorphism is the act of assigning distinctive human-like traits, feelings, and behavioral characteristics to non-human entities. The phenomenon known as artificial intelligence (AI) anthropomorphism involves imputing human-like behavioral characteristics onto generative artificial intelligence systems. This phenomenon holds significant implications for the future of human-human social interactions in society. This review paper examines the concept of AI anthropomorphism and its influence on human behavior, with a particular emphasis on how interaction between AI and humans can affect societal dynamics and social relationships among humans. Methodology: This paper examines the comprehensive understanding of AI anthropomorphism and the impact of AI-human interactions on human-human social interactions through the examination of several theoretical frameworks and empirical studies. The paper synthesizes information from the research literature on AI anthropomorphism. The paper incorporates insights from theoretical frameworks such as social presence theory, media equation theory, attachment theory, and uncanny valley theory. The paper entails an in-depth study of scholarly publications, case studies, and observational studies that highlights the implications for human relationships with anthropomorphized AI. Findings: The findings indicate that attributing human-like characteristics to AI can greatly increase user engagement, inclusivity, and understanding of AI, potentially enhancing human-human relationships by facilitating similar positive social behaviors. Excessive dependence on AI for social interaction can potentially diminish the quality of human communications and cause the erosion of social skills, thereby emphasising the importance of incorporating AI in a balanced manner. In conclusion, AI has the potential to enhance empathy, compassion, and teamwork in human communication. It is essential to strike a balance to avoid becoming overly reliant on generative AI and sacrificing authentic human connections. Subsequent investigations should prioritize the refinement of AI design and social chatbots to bolster and amplify human-human connections, rather than supplanting them.

The ship wreck of the RMS Titanic is still remembered as a well-known tragedy that took many lives. Using passenger data to predict who would survive this disaster presents an intriguing challenge for machine learning. This research utilizes the Random Forest algorithm, an effective ensemble learning technique, to examine and forecast survival outcomes based on factors such as age, gender, ticket class, and fare. Through thorough data preprocessing, which includes addressing missing values and creating new features, The model constructed delivers precise survival predictions. Important factors like passenger class and gender emerge as the most influential elements affecting the results. The model achieves a conclusion of over 82%, surpassing conventional machine learning methods like Logistic Regression and Decision Trees. By prioritizing feature significance and ensuring the model's broad applicability, this study not only emphasizes the predictive capabilities of machine learning but also provides insights into the societal and structural dynamics at play during the tragedy. Our results illustrate the effectiveness of Random Forest for binary classification tasks and its potential for wider use in predictive analytics.

Accurate and early detection of brain cancer is critical for improving treatment outcomes and patient survival. However, traditional diagnostic methods relying on radiological interpretation often lead to variable accuracy and delayed diagnoses due to the complex nature of brain tumors. This paper presents a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Recurrent Neural Networks (RNNs) for temporal analysis, specifically designed to improve brain cancer detection from MRI and CT scans. By leveraging the strengths of both CNNs and RNNs, the model captures intricate spatial and temporal patterns in medical images, leading to significant improvements in detection accuracy, sensitivity, and specificity. Comparative evaluations show that the proposed hybrid model outperforms conventional diagnostic techniques and existing deep learning approaches. The results highlight the potential of this method for earlier and more reliable brain cancer diagnoses, ultimately contributing to more personalized and effective treatment plans. Furthermore, the paper suggests that this hybrid approach could be adapted for the detection of other complex medical conditions.

Lung cancer is one of the top causes of cancer-related fatalities worldwide, necessitating the development of efficient early detection techniques. This study explores a hybrid approach combining deep learning and a Self-Organizing Map (SOM) for the classification of three lung cancer subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine tumors, using microscopic images. A pre-trained MobileNet model is employed for feature extraction, while the SOM is used for dimensionality reduction and visualization of high-dimensional data. The extracted features are then classified using various machine learning algorithms, including Random Forest, LightGBM and Decision Tree. A comparative analysis of these classifiers is conducted to assess their performance in predicting cancer types. Additionally, thresholding is applied to highlight cancerous regions in the images, enhancing the visual detection of malignant cells. Results indicate that the hybrid model provides competitive classification accuracy, with the Random Forest and Decision Tree classifiers showing particular promise. This research demonstrates the potential of combining deep learning with traditional machine learning techniques for lung cancer detection, offering a pathway toward more accurate and efficient diagnostic tools.

Tomatoes contributed about 232 billion Indian rupees to the Indian economy in the financial year 2020; it is next to potatoes in vegetable production in South Asian countries. Tomatoes are the most familiar vegetable crop, extensively cultivated on cultivated land in India. The tropical weather of India is relevant for development, but specific weather conditions and several other features affect the standard progress of tomato plants. Besides these weather conditions and natural disasters, plant disease is a big crisis in crop production and plays a vital role in financial loss. The typical disease detection approaches for tomato crops cannot produce a predictable solution, and the recognition period for diseases is slower. A primary recognition of disease provides optimum solutions compared to the existing detection methods. Recently, distinct technologies such as AI, IoT, pattern recognition, computer vision (CV), and image processing have quickly developed and been executed for agriculture, specifically in the automation of disease and pest detection procedures. CV-based technology deep learning (DL) approaches have been performed for previous disease detection. This study proposes a wide-ranging investigation of the disease detection and classification approaches inferred for Tomato Leaf Detection. This work also reviews the advantages and disadvantages of the methods presented. Additionally, the advancements, challenges, and opportunities are discussed in this field, providing insights into the recent methods. This survey is an appreciated resource for practitioners, researchers, and stakeholders involved in tomato cultivation and agricultural technology.

Kidney disease identification with machine learning and data science is transforming patient consideration and early diagnosis by using predictive models to identify important risk factors and biomarkers. There are several organs in the human body that performed vital functions. The kidney is a vital organ that removes toxic substances from the body, filtering blood. The reason for this is that the kidney is considered to be one of the important body parts. To maintain the health of the body, the kidneys should be safeguarded. Which kidney is affected by a different illness depends on a number of factors. The reason behind renal illness appears to be different in different individuals. The renal disease dataset (obtained via Kaggle) has been subjected to machine learning in this investigation to identify indicators of kidney illness. The primary goal of the data study has been to identify the core sources of the data, which has allowed for the distinction of any negative consequences. To choose the fundamental attributes of the data in this case, the connection component has been used. The data has been concluded using those foundational credits, and the implications of machine learning classifiers have begun kidney disease diagnosis.

This review paper explores the integration of Artificial Intelligence (AI) in Intrusion Detection Systems (IDS), highlighting how AI enhances the effectiveness and efficiency of these systems. It covers the evolution of IDS, from traditional methods to advanced AI-based techniques, including machine learning and deep learning. The paper compares these methods, assessing their strengths and weaknesses in various cybersecurity contexts. The focus is on the transformative impact of AI on IDS, offering insights into future research directions and the potential of AI to revolutionize cybersecurity defenses.

The escalation of sophisticated malware threats necessitates innovative solutions for their detection and neutralization. This paper discusses the role of Artificial Intelligence (AI) algorithms in the field of malware analysis, examining various AI methodologies, and scrutinizing their efficiencies and drawbacks. We further discuss the key AI algorithms utilized, their applicability, and future potential. This study provides a valuable resource for researchers and practitioners seeking to utilize AI for improved malware detection and mitigation.

In this paper, we propose a novel framework for evaluating the speaking quality of educators using machine learning techniques. Our approach integrates both audio and video data, leveraging key features such as facial expressions, gestures, speech pitch, volume, and pace to assess the overall effectiveness of a speaker. We collect and process data from a set of recorded teaching sessions, where we extract a variety of features using advanced tools such as Amazon Rekognition for video analysis and AWS S3 for speech-to-text conversion. The framework then utilizes a variety of machine learning models, including Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Trees, and Support Vector Machines, to classify speakers as either "Good" or "Bad" based on predefined quality indicators. The classification is further refined through feature extraction, where key metrics such as eye contact, emotional states, speech patterns, and question engagement are quantified. After a thorough analysis of the dataset, we apply hyperparameter optimization and evaluate the models using ROC-AUC scores to determine the most accurate predictor of speaker quality. The results demonstrate that Random Forest and Support Vector Machines offer the highest classification accuracy, achieving an ROC-AUC score of 0.89. This research provides a comprehensive methodology for automated speaker evaluation, which could be utilized in various educational and training environments to improve speaker performance.

This research looks into the design and realization of a model-driven application in Microsoft Power Apps for streamlining organizational processes. It brings together leave management, approval workflows, productivity tracking, task management and time logging into a single system. The software includes automated workflows that improve operational efficiency, transparency, and employee performance. The study goes over workflow and data flow structures needed to make a system efficient, detailed DFD for Data Flow Diagram tasks transform traditional manual processes — integration of business rules & Workflow Automation. The app can also integrate with your calendar, for easy access to all the responsibilities of an employee in one spot. Notifications and alerts make sure that important activity is not lost, while the reporting functionality gives managers a detailed understanding of what is going on. Automation of these processes, along with the rule based business logic which the app embeds greatly reduces manual labor and enables a standard process consistent for all employees throughout the organization. Ultimately, this project specifically addresses the root problem people have when trying to improve their operational efficiency, time management practices inside or outside of work and deliver tools tailored to provide better time data collection and alerting while supporting decisioning within the organization. The app is assessed by a research paper before the conclusion, evaluating it from the perspective of business and organizational goals.