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共 11827 条记录，以下是171-180 订阅

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Novel 6D Pose Estimation for Pick-and-Place Tasks using Model-based learning 3

Novel 6D Pose Estimation for Pick-and-Place Tasks using Mode...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Logesh, G. Vinoth, S. Pitchandi, Nagarajan Karthikeyan, A.G. Bannari Amman Institute of Technology Sathyamangalam India Department of Mechatronics Akshaya College of Engineering and Technology Coimbatore India Department of Mechatronics Bannari Amman Institute of Technology Sathyamangalam India School of Mechanical Engineering REVA University Bangalore India

ISBN: (纸本)9798331527549

The paper discusses the problem of creating an autonomous SCARA robot that can precisely carry out pick- and-place operations in unstructured settings. The suggested method combines computer vision and deep learning algorithms to provide accurate 6D pose estimation and reliable object detection. Combining YOLOv8 for object detection, hand-eye calibration for precise transformation to robot coordinates, and Principal Component Analysis (PCA) for orientation estimation within the generated point cloud, a novel method for estimating object pose is presented. Monocular depth estimation techniques are used to extract depth information from RGB images to create point clouds. The results demonstrate the potential of combining advanced algorithms with user-friendly design for robotics automation, showing notable gains in pose estimation accuracy and task execution efficiency. © 2025 IEEE.

关键词： Robotic arms

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Smart Sales Forecasting Machine learning Models for Demand Prediction in Retail 3

Smart Sales Forecasting Machine Learning Models for Demand P...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sandeep, V. Kandavel, N. Saivenkat, R. Gowri, R. Thirumalai Murugan, R. Zahid Hussain, J. Chennai Institute of Technology Chennai India

ISBN: (纸本)9798331527549

Forecasting accurately the upcoming product demand is important to the retail sector and is achieved by the timely use of stock and sales strategies. The study investigates how a machine learning model based on all the possible external variables other than the traditional historical sales data such as weather conditions, fuel prices, and previous year sales records can be used to predict sales trends. By the utilization of these external and time variables, the model comes up with a complete prognosis method, which is to be matched with the uncovering of possible patterns that conservative techniques might not identify. Examine a variety of machine learning methods, including time series analysis and regression models, to ascertain the technique that is the most suited to the study of the complex influences of consumer behaviour. Our findings show that by accounting for the external data from the economic field and environment that is related to the area, the accuracy of the forecast along with the adaptability of sales targeted at the involved company increase significantly, thus making it a very powerful tool. This predictive model not only enhances decision-making for inventory control but also enables retailers to better anticipate demand swings that are caused by such external factors, thus new merit is shared in strategic planning and operational efficiency. © 2025 IEEE.

关键词： Prediction models

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Empowering Communication: Emergency Hand Recognition for the Differently Abled 3

Empowering Communication: Emergency Hand Recognition for the...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Lavanya Rao, U.B. Rekha, P. Asuti, Manjunath G. Kumar, Sumit Nadaf, Mehak Bedre, Shashank K. Bnm Institute Of Technology Department Of Electronics and Communication Bangalore India Bnm Institute Of Technology Dept. of Ece Bangalore India Bnm Institute Of Technology Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331515911

The advancement of technology has significantly influenced the lives of differently-abled individuals, particularly in enhancing their communication and interaction capabilities. This article presents an innovative emergency hand recognition system designed to improve interaction and communication for individuals with disabilities. Utilizing advanced computer vision and machine learning techniques, the system identifies predefined hand gestures, facilitating quick and accurate emergency communication for those with speech or hearing impairments. By providing a dependable way for users to express urgent needs, this solution seeks to enhance the safety and independence of specially-abled individuals. The implementation of this technology not only empowers users in critical situations but also promotes greater inclusion in society, allowing for more effective interactions with caregivers and emergency responders. Thus, this work aims to bridge communication gaps and foster a more accessible environment for all. © 2025 IEEE.

关键词： Adversarial machine learning

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A Streamlined Method for Detecting Strawberry Leaf Diseases Utilizing Deep learning Techniques 3

A Streamlined Method for Detecting Strawberry Leaf Diseases ...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Singh, Jashandeep Singh, Gursharan School of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331527549

Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Networks classify strawberry leaf diseases while fixing problems found in earlier studies about speed and diagnosis precision. The proposed research evaluated five CNN models namely AlexNet, DenseNet, ResNet, MobileNet, and EfficientNet through implementation on a total of 6146 images that showcased both healthy and scorch-affected strawberry leaves. The proposed research optimized feature extraction techniques and added model comparison to find the most effective configuration. EfficientNet achieved higher accuracy and precision than all test models when detecting diseases in samples. The strong disease identification abilities of DenseNet and MobileNet show promise for developing better ways to manage agricultural health problems. This study expands precision agriculture methods while helping farmers sustain their operations through modern machine learning tools. © 2025 IEEE.

关键词： Fruits

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Deep Seizure: An Enhanced System for Detecting Epileptic Seizure in Real-time based on EEG Signals Utilizing Sliding Window-based Deep learning Approach 3

Deep Seizure: An Enhanced System for Detecting Epileptic Sei...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohideen, Akbar Badhusha Jhansi Lakshmi, D. Gopi Reddy, B. Jagadeesh, C.H. Kowsik, G. Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798331527549

Research explores epilepsy detection in real-time scenarios using a deep-learning framework with an innovative sliding window design approach. The primary challenge is that it is quite difficult to detect epileptic seizures in real-time using EEG signals accurately. Traditional methods are time-consuming and depend upon manual analysis of EEG data. In addition, variability in the seizure patterns and the presence of noise in EEG recordings can be complicated during the classification process. To overcome the problem, we will proceed with the following: The EEG signals will be taken as input, and with the help of CNN, the classification between seizure and non-seizure patients will be differentiated. The framework enhances the CNN implementation on the EEG recordings to evaluate the activity. The sliding window design will be helpful to make sure that the classes will be balanced within the training samples for improved accuracy. The outcome will be the enhancement of classification accuracy, and clinical decision-making, and the potential for future advancements in epilepsy management. © 2025 IEEE.

关键词： Decision making

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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AI/ML Applications in Spoofing Detection in 5G Networks: Challenges and Solutions 3

AI/ML Applications in Spoofing Detection in 5G Networks: Cha...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Singh, Monika Hegde, Raveesh Jha, Mahesh Kumar Choudhary, Amar Rubini, P. Cmr Institute of Technology Bengaluru India Alliance University Alliance College of Engineering and Design Bengaluru India Cmr University School of Engineering and Technology Bengaluru India

ISBN: (纸本)9798331515911

5G networks has advanced connectivity, high data, low latency and reliability. Along with these features new challenges come with respect to security, particularly spoofing attacks. One of the necessity in 5G and future wireless communication is to prevent the spoofing attacks depending on the applications. Artificial Intelligence (AI) and Machine learning (ML) present promising solutions for detecting and mitigating spoofing threats in 5G networks. This paper explains the different types of spoofing and applications, challenges, and future prospects of AI/ML in addressing spoofing in 5G networks. AI/ML algorithms can be used in detection of the location of attackers. This paper also analyses current methodologies, highlight key challenges, and discuss potential advancements that could enhance the security of 5G networks. © 2025 IEEE.

关键词： Adversarial machine learning

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Volumetric ResNet for Enhanced Lung Nodule Classification: A High-Accuracy Approach using Deep learning 3

Volumetric ResNet for Enhanced Lung Nodule Classification: A...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Asha, V. Bhavanishankar, K. Dept of Computer Science and Engineering RNS Institute of Technology Karnataka Bengaluru560098 India Visvesvaraya Technological University Karnataka Belagavi590018 India

ISBN: (纸本)9798331527549

Lung cancer remains a leading cause of mortality, affecting both men and women, has posed significant challenges for accurate identification for decades. Recent advancements in Deep learning (DL) have revolutionized the field of Computer-Aided Diagnosis (CAD) for detection and classification of lung cancer. This study introduces a novel approach leveraging Volumetric ResNet architecture for accurate classification of candidate Lung Nodules into Nodule and Non-Nodule categories. The pipeline begins with histogram equalization for enhanced Computed Tomography (CT) scan contrast, followed by Volumetric ResNet for classification with false positive reduction. Evaluation on the LUNA16 dataset demonstrates the superiority of the proposed model in terms of accuracy (99.49%), specificity (99.74%), sensitivity (100%), precision (99.75%) with augmentation and of accuracy (99.26%), specificity (98.89%), sensitivity (99.63%), precision (98.90%), False Positive Rate (FPR) 1.11% and False Negative Rate (FNR) 0.37% without augmentation, it surpasses the existing state-of-the-art methods. © 2025 IEEE.

关键词： Lung cancer

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PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Automatic Classification of Brain MRI slices into Glioma/Meningioma using Deep Transfer-learning 3

Automatic Classification of Brain MRI slices into Glioma/Men...

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vickram, A.S. Mathan Muthu, C.M. Saveetha School of Engineering Department of Biosciences TN Chennai602105 India Simats Saveetha School of Engineering Department of Biosciences TN Chennai602105 India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Brain tumor (BT) is a harsh brain abnormality and early detection and treatment is essential to reduce the impact of the disease. This work proposed a Deep Transfer-learning (DT) approach to detect the Glioma/ Meningioma with a better accuracy. The stages of this work includes;image collection and modifying its dimension to 224x224 pixels, feature extraction using chosen DT-models, feature reduction and serial features integration to get the fused-features, and bi-level classification using SoftMax and verifying the merit using 5-fold cross validation. This work initially executed the detection using individual features and then the experiment is repeated using the fused-feature vector (FFV). The FFV based detection helps to achieve a detection accuracy >97% on the chosen task and this outcome confirms the clinical significance of the proposed technique. In this work, the detection is performed using only the axial-plane 2D MRI slides, and in future, it can be extended to clinical MRI examination task. © 2025 IEEE.

关键词： Image acquisition

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