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A Review of Various Deep Learning Approaches for Placement Prediction 2

A Review of Various Deep Learning Approaches for Placement P...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Bodke, Minal Tiwari, Vishnu Wadhwani, Jayesh Gorbade, Trapti Soundane, Rohan Pimpri Chinchwad College of Engineering and Research dept. Computer Engineering Pune India

ISBN: (纸本)9798331505745

In recent years, predictive analysis for student placement has attracted a lot of attention, and machine learning approaches have shown to be useful in enhancing decision-making and accuracy in higher education. This review paper provides a conceptual framework to improve student placement predictions by using deep learning models with self-attention mechanisms. The aim of this model is to tackle problems related to feature importance selection, imbalanced datasets, and complex data patterns. We suggest a multi-tiered strategy that combines deep learning's ability to identify complex correlations in instructional datasets with ensemble learning techniques for reliable performance. Predictive quality will be thoroughly examined through the use of performance indicators such as accuracy, precision, recall, and F1 score in the evaluation of the models. It is anticipated that this strategy will give educational institutions more accurate forecasting instruments, revealing important factors influencing students' employability while guaranteeing decision-makers can understand the data. This framework intends to make a contribution to the expanding field of educational data mining and placement prediction by improving existing approaches. © 2025 IEEE.

关键词： Deep learning

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AI-Driven Solutions with CNN and IoT for Enhancing Visual Impairments 5

AI-Driven Solutions with CNN and IoT for Enhancing Visual Im...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Prasad, M. Lakshmi Varshitha, Veeramalli Goud, A. Praneeth Bindu, R. Srinivasulu, Ch. Rao, S. Janardhana Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Dundigal Hyderabad India

ISBN: (纸本)9798331522100

Visual impairment is a problem that affects millions of people worldwide, preventing them from walking independently and performing daily tasks. Some of the challenges that blind people face include crossing the street, reading, driving, and socializing. Despite the scientific community's continuous efforts to treat the eye, these solutions have not yet been found. Traditional aids such as canes are useful but limited. Get in the groove. The main focus of the project is to integrate the YOLOv5 object detection model with a convolutional neural network (CNN). Known for its fast and accurate target detection, YOLOv5, together with CNN, improves the physical ability to detect and classify targets in difficult areas with high accuracy. The eye uses the camera module to continuously monitor the environment and identify various objects in real time;the detected data is converted into speech by text-to-speech (TTS) converters such as Google Text- to-Speech (gTTS) and pyttsx3 This description allows users to get an instant description of their surroundings, unlike helping the blind, it improves their ability to navigate independently rather than just providing references, but there are also good ways to work with better and more reliable assistive technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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Macula Swelling Detection using Retinal Fundus Images

Macula Swelling Detection using Retinal Fundus Images

引用

2023 IEEE International Conference on Imaging Systems and Techniques, IST 2023

作者： Khan, Rukham Mumtaz Ali Khan, Fazal Saadat Khan, Fauzia Hassan, Rafia National University of Sciences and Technology Dept. of Computer Engineering Islamabad Pakistan University of Tartu Dept. of Computer Science Tartu Estonia Bahria University Dept. of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350330830

Diabetic Macular Edema (DME) is an advanced symptom of Diabetic Retinopathy (DR) and can lead to irreversible vision loss. Macular Edema (ME) is the swelling of the macula, the part of the eye accountable for thorough central vision. The utilization of image processing in the detection of diabetic macular edema (DME) plays a crucial role in the field of medical imaging, effectively reducing the time and manual effort required by medical specialists to identify this disease. In this paper, we proposed a method that uses digital fundus images to analyze the swelling of the macula. The proposed algorithm consists of four steps, i.e., (i) retinal image dataset collection, (ii) retinal image pre-processing, (iii) feature extraction, and (iv) classification to categorize the disease into three different stages, i.e., normal, stage 1 (mild) and stage 2 (severe). The proposed method is tested on three databases, i.e., MESSIDOR, DMED, and the local dataset AFIO obtained from the local hospital in Pakistan. We use three parameters to check the validity of the proposed algorithm, i.e., visual inspection, accuracy, and area under calculation time. Our method achieves an average accuracy of 96.07 % with a sensitivity of 93.74 % and specificity of 97.32%. We also compare our results with other proposed methods, which shows our method performs better. © 2023 IEEE.

关键词： Eye protection

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Single Image Super-Resolution Reconstruction Based on Multi-Scale Attention Mechanism 16

Single Image Super-Resolution Reconstruction Based on Multi-...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Su, Jin-Sheng Zhang, Ming-Jun Yu, Wen-Jing Dept. of Network Technology Software Engineering Institute of Guangzhou 510990 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This study is dedicated to solving the problem of single-image super-resolution reconstruction, particularly by introducing a multi-scale attention mechanism to enhance the reconstruction effectiveness. Advances in super-resolution technology have provided unprecedented possibilities for reconstructing images with low clarity, yet challenges regarding reconstruction quality and efficiency remain. Based on a review of the literature concerning attention mechanisms and their applications in super-resolution, this paper proposes a new network architecture that incorporates a carefully designed multi-scale attention module. The research demonstrates through experiments that the new algorithm can effectively capture the feature information of images and achieve improvements in detail recovery. Both quantitative and qualitative experimental results indicate that the proposed method outperforms existing approaches in the task of super-resolving images, showcasing significant advantages and practical application value. The paper also provides a detailed analysis of key modules within the algorithm and discusses the results obtained. Overall, this research successfully opens up new avenues for enhancing image reconstruction performance and offers valuable insights for future work. © 2025 SPIE.

关键词： Image reconstruction

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Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection 24

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Odeh, Suhail Samara, Anas Zreineh, Nicola Obeid, Seif Ibrieghieth, Maen Obaid, Mahmoud Bethlehem University Software Engineering Dept. Palestine Arab American University Computer System Engineering Jenin Palestine

ISBN: (纸本)9798350384307

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care. © 2023 IEEE.

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Posture Recognition-Based Robot Control Using Media Pipe Pose

Posture Recognition-Based Robot Control Using Media Pipe Pos...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo Kim, Jong Yeong Mun, Seo Park, BoJeong Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Electronic Engineering Busan Korea Republic of Dong-A University Dept.of Mechanical Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This study proposed a body movement-based system for efficiently controlling robots in large indoor environments. Traditional hand gesture recognition systems experience significant accuracy loss in large indoor spaces, particularly as the distance between the user and the camera grows. To address this issue, clear and large body movements were used as control signals instead of hand movements. For example, participants could give commands to the robot by raising both hands or lifting one hand in a specific direction. The system utilized MediaPipe Pose to track the user's posture in real time and communicated commands from the server to the robot via TCP/IP socket communication. This structure ensured stable data transmission, enabling smooth robot control even in expansive spaces. Consequently, this research demonstrated the feasibility of effective robot control in large indoor areas, such as screen golf courses, and suggested the potential for system expansion in various indoor environments. © 2025 IEEE.

关键词： Human robot interaction

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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Goyal, Diya Guru Prasad, M.S. Garg, Ishika Tewari, Shashank Pandu N, K. Asha, P.N. Dept. of Computer Science and Engineering Dehradun India Srinivas University Institute of Engineering & Technology Dept. of Computer Science and Engineering Surathkal India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371314

Technology is very volatile in nature, in a way that it keeps on changing daily. Restaurants are no different in the effects of this changing scenario. The primary objective of this research paper is to explore the historical trajectory of restaurants, analyze the current trends shaping the industry, and delve into the potential of chatbot integration to overcome existing challenges. Chatbots can play a pivotal role in enhancing the performance of businesses as per the research done. An integrated Dialog Flow application has been proposed, incorporating Rule-based grammar matching and M.L matching algorithms. This proposed work is trying to make NLP-based chatbots which may expedite the endeavor of businesses to produce and become profitable. © 2024 IEEE.

关键词： Python

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening 3

Bio-inspired Heuristic Optimization-based Cascaded Network f...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Shuvo, Eshat Ahmad Rahman, Wahidur Hossain, Md Shakhawat Islam, Md. Tarequl Iqbal, Md. Sadiq Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Center Independent University Dept. of Computer Science & Engineering & RIoT Bangladesh Khwaja Yunus Ali University Dept. of Computer Science & Engineering Sirajganj Bangladesh

ISBN: (纸本)9798350388282

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features;the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.

关键词： Large datasets

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Disease Detection And Severity Prediction Of Pomegranate Fruit 15

Disease Detection And Severity Prediction Of Pomegranate Fru...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paul, Poulin Manishankar, S. Satheesh Kumar, R. Sahrdaya College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Thrissur India Jawaharlal College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Palakkad India

ISBN: (纸本)9798350370249

This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases - Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora - are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. Then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. The area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. The segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction. © 2024 IEEE.

关键词： Annotation Convolutional Neural Networks Deep learning Semantic segmentation Unet

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An Analysis of Image Enhancement Techniques

An Analysis of Image Enhancement Techniques

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Sharma, Reena Jain, Nikita Tiwari, Sudhanshu Gothi, Astha Manipal University Dept. of Computer& Comm. Engineering Jaipur India Poornima College of Engineering Dept. of Advance Computing Jaipur India Poornima College of Engineering Dept. of Computer Engineering Jaipur India

ISBN: (纸本)9798350348460

A crucial aspect of image processing is image enhancement. Image enhancement techniques can be used to improve the image's quality and aesthetic appeal in order to more accurately convey a transform. In general, multiple types of photos, including satellite images, medical images, and photographs taken in real life, are impacted by low contrast and unique noise. A picture can be enhanced or modified using the filtering process. For instance, we may filter an image to highlight some elements while removing others. Filtering is used to accomplish image processing operations including edge enhancement, sharpening, and smoothing. Image enhancement, filtering, segmentation etc. all are considered in image preprocessing. © 2024 IEEE.

关键词： edge enhancement filtering Image enhancement modifying

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