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A Novel Approach to Identify Pituitary Tumor Using an Optimal CNN Model with Deep Learning

A Novel Approach to Identify Pituitary Tumor Using an Optima...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Ebin, P.M. Thomas, Roja Ismail, Safad Kavitha Nair, R. Alliance School of Advance Computing Alliance University Banglore India Mangalam College of Engineering Department of Computer Science and Engineering Ettumanoor India College of Engineering Chengannur Department of Computer Engineering Chengannur India Acharya Institute of Technology Department of Ai &ml Banglore India

ISBN: (纸本)9798350313796

The technical paper introduces a novel approach to identifying pituitary tumors using an Optimized Convolutional Neural Network (OCNN) model with deep learning. Pituitary tumors rank among the most prevalent variety of brain tumors, and accurate identification is critical for proper diagnosis and treatment. The suggested method makes use of a deep learning framework that combines an ideal feature extraction method with a pre-trained CNN model. The pulled out features are then used to train the CNN model to classify pituitary tumors accurately. The effectiveness of the suggested method is assessed using a publicly accessible dataset, revealing that it surpasses the performance of current leading techniques. The suggested method possesses the capacity to act as a valuable asset for making clinical decisions in the field of neuroimaging, particularly for the accurate identification of pituitary tumors. The results of the experiment indicate that our suggested model successfully detects and classifies tumors with high accuracy, sensitivity, and specificity. Our model specifically achieves a 99.55% overall accuracy, 99% sensitivity, 99% specificity and 99% F1 score. These outcomes show that our suggested CNN design for brain tumor identification and classification performs better than several state-of-the-art techniques. © 2023 IEEE.

关键词： Tumors

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Enhancing Customer Retention through Feature Selection and XGBoost Classification for Churn Prediction

Enhancing Customer Retention through Feature Selection and X...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Raye Haarika Ayain John Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Customer churn remains a significant and pressing challenge for both large enterprises and businesses across various industries. Particularly within the banking sector, where revenue is directly influenced by customer retention, there is a concerted effort to enhance predictive methods for identifying potential churn. This paper explores different machine learning (ml) algorithms employed in constructing a churn model to assist banking operators in predicting customers at risk of churning using minimum attributes and aiming to achieve good accuracy. The research delves into experimental results to compare the efficacy of various techniques, identifying the model that works best for predicting customer attrition in the banking sector in the end. This study uses feature selection and the XGBoost algorithm to select attributes based on their ranks.

关键词： Training Industries Accuracy Costs Computational modeling Machine learning Banking

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Classification of DDoS Attacks Using Ensemble Learning

Classification of DDoS Attacks Using Ensemble Learning

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Adepu Sai Aashrith Vishwaroju Sirichandana Barka Tarun Kumar M A Jabbar Y Sridevi Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Department of English Vardhaman College of Engineering

In an era where the digital realm is indispensable to daily life, Constantly posing a threat, distributed denial of service (DDoS) attacks are capable of disrupting vital online services. Traditional methods of DDoS attack classification often fall short when facing the ever-evolving landscape of attack techniques. This paper unveils a journey through the complexities of enhancing DDoS attack classification with a focus on improving accuracy and adaptability. We embarked on this research by individually training a plethora of machine learning models. However, our preliminary findings highlighted the striking dis-parity in accuracy across different models, highlighting how complex and varied DDoS attacks are. Driven to overcome these constraints, we resorted to the sturdy framework of ensemble learning. Ensemble learning changed the game for accurate DDoS attack classification by effortlessly combining the best features of several base models. To be more precise, we blended two distinct baseline models: K-Nearest Neighbors (KNN) and Random Forest (RF). These models, each with distinct characteristics, combined to form a flexible and adaptive DDoS attack classification system. K-Nearest Neighbors (KNN) uses the proximity of data points to find trends and parallels. Resilience, high-dimensional data processing, and overfitting resistance are all favoured by the Random Forest (RF) ensemble of decision trees. Combining these several methods into a group greatly improved our categorization accuracy. Our research's ability to forecast accuracy using the ensembled model was one of its main results. This prediction accuracy indicates the flexibility and dependability of the classification model in addition to providing a crucial performance standard for assessing system effectiveness. Finally, our explo-ration of the DDoS assault classification demonstrated Ensemble Learning's superiority. The proposed strategy surpassed the existing methodologies, according to experimental

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Enhanced Large Language Models

Enhanced Large Language Models

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International Conference on Inventive Computation Technologies (ICICT)

作者： J.R.V. Jeny G. Bharath Babu S. Jaganmohan Reddy N. Muthukumaran K. Abhiram Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This work studies ways for improving the effectiveness of a language model in creating and understanding dialogue summaries. Two primary approaches are investigated, in the Complete Fine-Tuning step, a pre-trained FLAN-T5 model is fine-tuned using a dataset of dialog-summary pairs. The dataset is pre-processed to provide explicit instructions to the model, and the fine-tuning process involves training the entire model on the dataset. Qualitative and quantitative evaluations are then conducted to assess the effectiveness of the fine-tuned model in generating summaries. In the PEFT phase, a more computationally efficient method is employed, utilizing Low-Rank Adaptation (LoRA) to train a new layer/parameter adapter while keeping the underlying FLAN-T5 model frozen. This approach significantly reduces computational resources compared to Complete Fine-Tuning while maintaining comparable performance. The resulting model, equipped with the PEFT adapter, is evaluated qualitatively and quantitatively to measure its summarization capabilities. Both approaches sheds light on the compromises between computing efficiency and model performance.

关键词： Training Measurement Adaptation models Generative ai Computational modeling Natural languages Information retrieval

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A Comparative Study of Machine Learning Algorithms for Predicting Customer Churn: Analyzing Sequential, Random Forest, and Decision Tree Classifier Models

A Comparative Study of Machine Learning Algorithms for Predi...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Ratnam Dodda C. Raghavendra Marpu Aashritha Hima Varshini Macherla Anusha Reddy Kuntla Department of CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE (DS) CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology CVR College of Engineering Hyderabad India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Predicting customer churn is vital for businesses aiming to retain their customer base and enhance profitability. This study investigates the effectiveness of three prominent machine learning algorithms Sequential, Random Forest, and Decision Tree Classifier in forecasting customer churn. Utilizing a dataset encompassing historical customer information such as demographics, transaction records, and interaction patterns, we preprocess the data by addressing missing values, encoding categorical variables, and scaling numerical features. Subsequently, each model is trained on the processed dataset, and their performance is assessed using accuracy as the evaluation metric. Our findings demonstrate promising predictive capabilities, with the Sequential model achieving an accuracy score of 94%, followed by Random Forest with 92%, and Decision Tree Classifier with 91%. Additionally, we conduct a comparative analysis of these models, focusing on interpretability, computational complexity, and robustness. While the Sequential model excels in predictive accuracy by capturing temporal dependencies in sequential data, Random Forest and Decision Tree Classifiers offer simplicity and interpretability, catering to scenarios where model transparency is paramount.

关键词： Analytical models Accuracy Machine learning algorithms Computational modeling Predictive models Prediction algorithms Data models Numerical models Decision trees Random forests

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Soft Computing based Implementation of Autonomous Drone Vehicle using Fuzzy Logic based Control Systems 2

Soft Computing based Implementation of Autonomous Drone Vehi...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICaiSS 2023

作者： Mishra, Soni Punitha, S. Harikrishns, T. Swamy, K. Narayana Loganayagi, S. Graphic Era Hill University Department of Physics Dehradun India Department of Mathematics Tamil Nadu Salem India Annamacharya Institute of Technology & Sciences Department of Ai&ml Andhra Pradesh Rajampet India Kattigenahalli Yelahanka Bengaluru North Bengaluru560064 India Department of Computer Science and Engineering Tamil Nadu Thandalam Chennai602105 India

ISBN: (纸本)9798350325799

In recent years, there has been a lot of interest in the use of fuzzy logic-based control systems to construct autonomous drone vehicles. This method makes use of fuzzy logic's adaptability and flexibility to guide drones safely through challenging surroundings. This study gives a comprehensive framework for the design and implementation of fuzzy logic-based control systems for autonomous drone vehicles. The three primary parts of the proposed framework are perception, decision-making, and control. The perception module processes sensor input, including visual and depth data, to provide accurate representations of the world. The decision-making module interprets the perception data and uses fuzzy logic methods to arrive at sensible choices for navigation and obstacle avoidance. The fuzzy logic system's decisions are converted by the control module into the right control signals that the drone will follow. The fuzzy logic-based control system also shows resilience and adaptation to changing environmental circumstances and uncertainties, making it appropriate for real-time applications. New opportunities for autonomous drone applications, such as surveillance, delivery services, and search and rescue missions, are made possible by the adoption of this architecture. Using fuzzy logic-based control systems to create autonomous drone vehicles is a viable way to provide effective and precise drone navigation in challenging conditions. By offering a holistic framework that blends perception, decision-making, and control utilizing fuzzy logic approaches, this research advances the development of autonomous systems. © 2023 IEEE.

关键词： Decision making

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Fake Account Detection Using ANN Based Model in Machine Learning

Fake Account Detection Using ANN Based Model in Machine Lear...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Y. Alekya Rani Ganja Srividya Allam Balaram Kotha Harish Kumar Ajmeera Kiran Manda Silparaj Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad Dept of CSE Stanley College of Engineering & Technology for Women Hyderabad Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer science Engineering (Data science) CVR College of Engineering Hyderabad Department of Computer Science and Engineering (CS) CVR College of Engineering Hyderabad

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In the digital age, the proliferation of fake accounts on various platforms poses significant challenges in terms of cybersecurity, misinformation, and online harassment. To combat this issue, this project aims to develop a robust fake account detection system using machine learning techniques. The project involves the collection of user data, feature engineering,data preprocessing, and the selection of suitable machine learning algorithms. The model is trained and evaluated using relevant metrics to distinguish between real and fake accounts effectively. Once deployed, this system continuously monitors user activities and alerts administrators when fake accounts are detected, enabling timely and legal compliance. The success of this project depends on the quality and quantity of data as well as the ability to adapt and improve the detection methods over time, in a constant battle against evolving fake account creation techniques.

关键词： Measurement Machine learning algorithms Social networking (online) Law Artificial neural networks Forestry Information age

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Deep Reinforcement Learning for IoT-Based Smart Traffic Management Systems 7

Deep Reinforcement Learning for IoT-Based Smart Traffic Mana...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kansal, Vipashi Shnain, Ammar Hameed Deepak, A. Rana, Aditya Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamil Nadu Chennai India Uttar Pradesh India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

The increasing complexity of urban traffic networks demands more intelligent and adaptive solutions for traffic management. This paper presents a novel approach utilizing Deep Reinforcement Learning (DRL) in conjunction with Internet of Things (IoT) technology to develop a smart traffic management system aimed at optimizing traffic flow in real-time. IoT devices, including sensors, cameras, and connected vehicles, provide comprehensive, high-resolution traffic data, which is processed by a DRL-based algorithm to dynamically control traffic signals and manage congestion. The proposed system continuously learns from evolving traffic patterns, adapting its decision-making to optimize key metrics such as vehicle throughput, travel time, and energy consumption. Compared to traditional traffic management systems, the DRL-based approach exhibits superior performance in handling dynamic traffic environments, reducing bottlenecks, and minimizing delays. Extensive simulations demonstrate that the integration of DRL with IoT enhances the system's ability to manage complex, real-world traffic scenarios more efficiently. This research contributes to the growing body of work in smart city development, offering an innovative framework for the deployment of intelligent traffic management systems. The study highlights the potential of DRL and IoT technologies to transform traffic management, promoting sustainable urban mobility and improving overall traffic efficiency.. © 2024 IEEE.

关键词： Travel time

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Automated Online Examination System using Hybrid Particle Swarm Optimization and Grey Wolf Optimization Algorithm 2

Automated Online Examination System using Hybrid Particle Sw...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Rana Veer Samara Sihman Bharattej, R. Rupavath, Rana Veer Samara Sihman Brahmatej Ramadan, Ghazi Mohamad Khanam, P Katheeja Sharma, Neeraj Saint Leo University Department of Computer Science St Leo United States Indiana Wesleyan University Marion United States Ahl Al Bayt University College of Mlt Karbala Iraq Mohan Babu University School of Computing Dept. of Cse Tirupati India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350372892

Artificial intelligence has been successfully used in many fields, conserving an enormous quantity of money and human resources. System of intelligent examination is a common application case that enables teachers to develop further research plans with the examination system's assistance along with analyzing each candidate's study conditions at any given time. This work implements a hybrid particle swarm optimization with grey wolf optimization (HPSOGWO) based self-optimization examination system. Two challenges that the traditional examining system overcame in its development can be addressed by the intelligent examination system: maintaining the examination question database and identifying attributes of examination questions. Data pre-processing is performed by utilizing data cleaning, and data integration for the evaluation of filtered text effectively. To prevent generating challenge an examination paper, a mathematical model is performed. The experiment demonstrate that the new approach can intelligently optimize the properties of exam questions in database and efficiently maintain examination database through extensive training. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Analysis of Magnetic Resonance images for the detection of Alzheimer's 4

Analysis of Magnetic Resonance images for the detection of A...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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