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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Karthikeya, Andhoju Kumar, Alle Aniesh Manicharan, Chennuri Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350303360

The recommendation system is one of the bigger developments brought about by machine learning in the machine-human interactions. In practically every area, including entertainment, e-commerce, farming, health, etc., recommendation systems are the primary players in delivering greater personalization. When it comes to content-based recommendation systems, there are many social platforms which uses recommendation system for providing improved personalization to their users and to access these social platforms the user need to have an account in that particular social platform. These platforms have different categories of content where not everyone is eligible to a particular content category. There are scenarios where different users may use the same account to access an entertainment application, or if a user inadvertently watched some inappropriate content, the recommendation system will make inappropriate recommendations, which can have an impact on the user's mental health-children. Therefore, after conducting research on the problem statement and reviewing various existing research works, machine learning model is proposed that offers recommendations to the user after determining the user's age and gender in order to deliver appropriate recommendations to the users. © 2023 IEEE.

关键词： Recommender systems

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Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

Space robotics hybrid conceptual model for tracking and esti...

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作者： Sinha, Anurag Garg, Namit Singh, N.K. Kumar, Biresh Banerjee, Pallab Verma, Achyut Kumar Department of Computer Science IGNOU Delhi India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Computer Science and Engineering Amity University Jharkhand Ranchi Jharkhand India Department of Chemical Engineering Indian Institute of Technology Kanpur India

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated. © 2023

关键词： Reinforcement learning

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A Novel Framework Leveraging Adam Optimization Techniques, Coupled with Mask R-CNN Deep Learning Mechanisms for Cervical Cancer Detection System 3

A Novel Framework Leveraging Adam Optimization Techniques, C...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Saranya, G. Sujatha, C. Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624 002 India Ssm Institute of Engineering and Technology Department of Computer Science Engineering Dindigul624 002 India

ISBN: (纸本)9798350375190

Cervical cancer remains a significant public health concern, with early detection crucial for successful treatment. Deep learning techniques offer promising avenues for automated cancer diagnosis, and this work explores the potential of the Mask R-CNN model with Adam optimization for cervical cancer detection in colposcopy images. This study aims to develop and evaluate a deep learning system based on Mask R-CNN and Adam optimization for automated detection and segmentation of cervical lesions in colposcopy images. The proposed system utilizes Mask R-CNN, a deep convolutional neural network (CNN) is capable of both object detection and instance segmentation. Pre-trained on the COCO dataset, the model is fine-tuned on a labeled colposcopy image dataset for cervical lesion detection and segmentation. Adam optimization, an efficient gradient descent algorithm, is used to maximize the training process of the model. High accuracy in recognizing and differentiating cervical lesions in colposcopy images is anticipated from the system. The system should demonstrate its ability to perform effectively on unseen data and handle variations in image quality. The proposed system's performance is evaluated on a separate test dataset using standard metrics like accuracy, sensitivity, specificity, and precision for both lesion detection and segmentation tasks. Additionally, the system's generalizability and robustness to variations in image quality are assessed. This novel approach has the potential to significantly contribute to the field of cervical cancer diagnosis by providing an automated, accurate, and efficient system for early detection, ultimately leading to better patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Mutual Coupling Reduction in MIMO Antenna for 5G Application by Self-Decoupled Method 2

Mutual Coupling Reduction in MIMO Antenna for 5G Application...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rashid, Aamir Shah, Syed Shahid Hussain, Irshad Jalal, Muhammad Ramalakshmi, R. Ramachandran, V. University of Engineering and Technology Faculty of Electrical and Computer Engineering Peshawar Pakistan College of Computer and Information Engineering King Saud University Department of Computer Engineering Riyadh Saudi Arabia Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798350347982

This paper shows a simple, compact and small 8×8 MIMO antenna. The ability of MIMO antennas is to broadcast signals in a various patterns and polarizations is extremely advantageous for contemporary communications systems. Excellent diversity gain (DG > 9.99dB), a low envelop correlation coefficient (ECC 0.002), and extremely low mutual coupling (S2l≥ 27) dB are all characteristics of the constructed MIMO antenna. S-boundaries, energy pattern characteristics, maximum gain, diversity gain, and envelop coefficient are used to evaluate the antenna performance. The mutual Coupling is the most basic fundamental parameter for the MIMO antenna, was likewise analyzed and it was observed that its value is under -27dB. self-decoupled MIMO antennas have a loop type ground antenna which reduces the requirement for extra decoupling structures or a postponed tuning method Antennas for ground radiation and loop antenna are magnetic-current and electric-current components, respectively. The suggested MIMO antennas feature a self-decoupled despite the antenna parts being near together, reduced profile and easy fabrication advantages the suggested, a revolutionary approach called MIMO antenna system is expected to offer excellent isolation and minimal mutual coupling, making it suitable for 5G applications. © 2023 IEEE.

关键词： MIMO systems

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Resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication 3rd

Resource Allocation in 6G Network for High-Speed Train Using...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Garg, Aakansha Arya, Rajeev Singh, Maheshwari Prasad Wireless Sensor Network Lab Department of Electronics and Communication Engineering Patna India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9789819743582

The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Doppler shift. To effectively communicate in high-speed train, the placement of a mobile relay (MR) with small server at the rooftop of train coach is proposed. The base station sends channel state information to mobile relay which stores it in the stack. The resource assignment has been conducted by an improved grey wolf optimization algorithm. The focus is at improving throughput with reduction in latency and energy consumption. The simulation outcomes manifests that the presented algorithm carry-out finer in comparison of the Hungarian algorithm. The accomplished results are refined almost three times in respect of energy consumption, latency, and throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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Towards application of positioning systems in the mining industry

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International Journal of Mining and Mineral engineering 2024年第1期15卷 15-48页

作者： Onifade, Moshood Zvarivadza, Tawanda Adebisi, John Adetunji Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC Australia Department of Civil Environmental and Natural Resources Engineering Luleå University of Technology Luleå Sweden Department of Electrical and Computer Engineering University of Namibia Namibia

Positioning and communication systems hold intriguing potentials in the context of the mining industry. The need for safety and environmental precautions in mining has grown clearer for governments and scientists across the world. This is in light of complex and hazardous mining situations which can be tackled by the advancement of science and technology. This paper discusses how positioning systems have enhanced mining operations' safety, productivity and environmental sustainability. The drawbacks of positioning systems in the mining industry are established and analysed by examining the influence this technology has had on the different operations in the mineral industry. The possibility of their utility for precise localisation in situations when no other techniques exist has been carefully evaluated for recent uses of these technologies in the mineral industry. Future study must address a few significant issues and gaps that have been identified. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Radio frequency identification (RFID)

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A Survey on Image Captioning Using Object Detection and NLP 4

A Survey on Image Captioning Using Object Detection and NLP

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： De Silva, Vathila Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology 06 Colombo Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

Recent years have seen the emergence of image captioning as a revolutionary technical development that seamlessly blends computer vision and natural language processing. The integration of various domains facilitates the production of captivating captions for pictures, promoting a more profound comprehension of visual material. This study offers a thorough analysis of the rapidly developing field of image captioning, examining its uses in various settings such as social media, online platforms, assistive technology, and content indexing. It emphasizes the critical importance of advanced techniques like You Only Look Once (YOLO) for accurate item detection and Natural Language Processing (NLP) for creating subtle captions. While NLP gives the resulting text a layer of contextual depth, YOLO guarantees precise object detection, which helps with caption accuracy. Using these cutting-edge methods boosts the overall efficacy of image captioning systems and represents a noteworthy trend in the literature. Examining the evolution of image captioning, the review paper highlights how important it is for multimodal comprehension. Image captioning is a transformative tool that enhances the readability of visual content and influences contemporary digital experiences in various contexts. The development of image captioning is summarized in this abstract, which also highlights the tendency to use cutting-edge techniques for more accurate and nuanced caption production. © 2024 IEEE.

关键词： Object detection

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Enhancing Syndicate Lending Through AI-Powered Fine-Tuning: Leveraging LLAMA2 for Dynamic Decision Support

Enhancing Syndicate Lending Through AI-Powered Fine-Tuning: ...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Kumar, Ashish Jindal, Kanika Sharma, Himanshu Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Uttar Pradesh Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

This electronic document describes the involvement of finance agencies and various types of corporate industries through 'syndicated loans' that simplify financial transactions for both agencies and corporations. This will be a revolutionary step towards the 'fintech' sector as it is all about decentralization and how it changes the whole game during the lending process. This paper describes a proposed process of lending, monitoring, evaluation, and development in the lending process between borrowers (enterprises) and lenders using Large Language Model. After highlighting the difficulties in implementing this proposal, the paper also discusses its impact on the transition process and outlines what has already been done. The proposal increases the transparency of the entire process among stakeholders, simplifies manual work, reduces operational costs, and provides data to support the analysis and summarization of benefits derived from the lending process. A secure system offers the ease of use and dexterity of the technology that makes the agenda highly reliable in all aspects of test cases. The study includes the experimentation of proposed system that how it reduces the failure rate of startups by 70% by providing financial aids to startups and suggesting a better business model if needed. © 2024 IEEE.

关键词： Finance

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Mitigating Sycophancy in Large Language Models via Direct Preference Optimization

Mitigating Sycophancy in Large Language Models via Direct Pr...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khan, Azal Ahmad Alam, Sayan Wang, Xinran Khan, Ahmad Faraz Neog, Debanga Raj Anwar, Ali University of Minnesota-Twin Cities Department of Computer Science and Engineering United States Indian Institute of Technology Guwahati India Virginia Tech Department of Computer Science United States

ISBN: (纸本)9798350362480

Large language models (LLMs) have demonstrated remarkable capabilities, yet they occasionally exhibit sycophantic behavior, generating responses that align with or agree with a user's stated opinions or preferences, even when those opinions are incorrect or biased. This sycophantic tendency can undermine the trustworthiness and reliability of LLMs. This work proposes a novel approach to mitigate sycophancy in LLMs by fine-tuning them on a carefully curated dataset comprising prompts paired with sycophantic and non-sycophantic responses 1. Our method leverages Direct Preference Optimization (DPO), which optimizes LLMs to generate responses that align with the preferred (non-sycophantic) outputs without requiring explicit reward modeling. We develop a dataset of 1000 prompts with sycophantic and non-sycophantic responses to fine-tune LLMs. Our approach achieves an average reduction of 85% in persona-based tests and 84% in preference-driven tests, demonstrating significant mitigation of sycophantic behaviors. Our findings pave the way for more trustworthy and reliable language models that can provide objective and unbiased responses, aligning with human preferences while maintaining factual accuracy. © 2024 IEEE.

关键词： Finetuning Large Language Models

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