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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Usha, V. Abhinash, Nalagarla Chiru Sathya, V. Kujani, T. Rao, P. S. Omkara Eswara Kishore, Koneti Leela Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Dept.of Cse Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept Of Computer Science And Business Systems Chennai India

ISBN: (纸本)9798350352689

Transportation operations in sparsely populated or rural areas are planned and organized in an efficient manner. The objective is to maximize the efficiency of transport service scheduling in order to cater to the movement requirements of the inhabitants in these regions. The aim is to optimize the functionality and efficiency of transportation services in rural regions by coordinating the available vehicles in a manner that satisfies the needs and preferences of the local populace while also taking into account logistical limitations. The implementation of machine learning incorporates a theoretical and data-driven strategy to enhance the transit timetable decision-making process. A dataset including vehicle capacities, available vehicle counts, and desired travel hours for people is essential to the project code. This project use unsupervised machine learning methods since the data does not have any labels. Clustering is a popular unsupervised machine learning technique because it groups similar data into meaningful categories. Among the clustering techniques that were used were K-Medoids, DBSCAN, Mini-Batch K-Means, and K-Means. Hierarchical clustering is chosen because it can keep the total number of clusters and the number of nodes within each cluster at a constant value. Once the data points have been aggregated using the algorithms, the silhouette score is used to choose the approach that is most suited for vehicle scheduling. The relevance of cluster centers determines the final scheduling of the vehicles. © 2024 IEEE.

关键词： Efficiency

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High-Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture

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Procedia computer science 2025年 258卷 2268-2277页

作者： Nitesh Patnaik Rishi Raj Piyush Kumar Singh Shreekara Mahapatra Debashis Pani Ravi Shankar Singh Dept. of Computer Science and Engineering Kalinga Institute Of Industrial Technology Bhubaneswar 751024 India Dept. of Computer Science Central University of South Bihar Bihar 824236 India Dept. of Computer Science and Engineering Indian Institute of Technology BHU Varanasi 221005 India

Deep learning-based image restoration faces significant challenges when deployed on resource-constrained platforms due to the computational demands and large number of parameters in existing models. The high computational load and extensive memory requirements make it difficult to implement these models on devices with limited processing power and storage capacity, such as mobile phones and embedded systems. Additionally, maintaining real-time performance while ensuring high-quality image restoration is a critical challenge, as traditional deep learning models often fail to meet the stringent latency and efficiency requirements of such platforms. This paper introduces a new, lightweight convolutional neural network (CNN) architecture tailored for efficient pixelation detection and restoration. Our approach combines a pre-trained MobileNetV2 with finetuning for pixelation detection, and SpectraNet, a architecture incorporating depth-wise separable convolutions for image restoration. The proposed architecture was trained on the HQ-50k dataset, with 70% of the data used for training and 30% for testing. It achieved a Peak Signal-to-Noise Ratio (PSNR) of 17-23, outperforming 10 state-of-the-art architectures in both efficiency and image quality. This design addresses the dual problems of high computational load and extensive memory usage, prioritizing real-time performance and resource efficiency. The proposed architecture is ideal for deployment on mobile and embedded devices since it maintains high accuracy while significantly reducing the number of trainable parameters. Our findings advance the feasibility of deploying advanced image restoration techniques in real-world, resource-constrained environments.

关键词： depth-wise Separable Convolutions Embedded Devices Image Restoration Lightweight Real-Time Performance Resource-Constrained Platforms Deep Learning

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Improved Dual Haptic Controller with Extra Virtual Damper for Z-Width Enhancement 3

Improved Dual Haptic Controller with Extra Virtual Damper fo...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Tiwari, Arpan R. Saha, S.K. Kumar, Subodh Indian Institute of Technology Delhi Dept. of Mechanical Engineering New Delhi India Indian Institute of Technology Delhi Dept. of Computer Science Engineering New Delhi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Haptic simulation systems use the sensation of touch to allow users to experience virtual surroundings. In interfaces that are based on impedance, haptics controllers are sampled data that use angular position and velocity to provide torque feedback. The sample and hold data collecting strategy, which connects the continuous and discrete worlds, hinders the haptic model's stability. Increasing the sampling rate enlarges the Z-width of the system but increases sampling frequencies, deteriorates the velocity information, reducing the stable range of damping. In this work, implementation of a concept similar to virtual coupling has been used by introducing an extra layer of the damper in between the virtual system and the Haptic device. Sampling the velocity at lower frequencies and position at higher one help in maximizing the stiffness range. This dual control of the improved architecture of the dual-rate controller is discussed in this paper. The suggested scheme's effectiveness was validated by theoretical stability analyses utilizing linear control methods and compared with passivity theory. © 2022 IEEE.

关键词： Controllers

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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Multiclass Categorisation of Respiratory Sound Signals Using Neural Network

Multiclass Categorisation of Respiratory Sound Signals Using...

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2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022

作者： Babu, Naseem Kumari, Jyoti Mathew, Jimson Satija, Udit Mondal, Arijit Indian Institute of Technology Patna Dept. of Computer Science and Engineering Bihar801106 India Indian Institute of Technology Patna Dept. of Electrical Engineering Bihar801106 India

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

Respiratory diseases have seriously impacted human life in the last couple of years;as Covid 19 arrived, many lost their beloved ones. Since respiratory diseases directly attack the patient's lungs, it is becoming risky day by day for human life and doctors because a confined number of resources are available in hospitals to detect these respiratory diseases, and detection of these diseases is a difficult job to the doctors. Therefore early-stage diagnosis can help the doctor in saving human lives. Researchers are continuously trying to help doctors by designing efficient and more accurate tools for detecting different types of respiratory diseases. This paper uses a convolution-based deep learning model to classify these respiratory diseases using patient respiratory sound signals with Mel frequency cepstral coefficients (MFFCs) as a feature vector. In this paper, we have tried to keep our neural network model as simple as possible with less trainable parameters and good classification accuracy. The model performance is measured in terms of sensitivity, specificity, average score, and harmonic score. © 2022 IEEE.

关键词： Convolutional neural networks

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images 1

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Padthe, Adithya Mathapati, Mahantesh Priyanka, M.S. Nandihal, Priya University of the Cumberlands Department of Information Technology United States Dept.of Cse India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350316469

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images. © 2023 IEEE.

关键词： Artificial plant optimization algorithm Convolutional Neural Network Endoscopic images Gastric cancer Multi-scale parallel convolution blocks Recurrent Neural Network

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350379525

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems. © 2024 IEEE.

关键词： Prediction models

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Enhancing Lymph Node Classification in Rectal Cancer MRI Images Through a Multi-Model Approach

Enhancing Lymph Node Classification in Rectal Cancer MRI Ima...

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2024 IEEE International Conferences of AI, science, engineering, and technology, AIxSET 2024

作者： Gupta, Rishit Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Bhatt, Manish Kijsirikul, Boonserm Wang, Aili Ouchi, Akira Shimizu, Yasuhiro Indian Institute of Technology Guwahati Dept. of Electronics & Electrical Eng. Assam Guwahati India Chubu University Dept. of Computer Science Kasugai Japan Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Chulalongkorn University Dept. of Computer Engineering Bangkok Thailand Harbin University of Science and Technology Higher Educational Key Laboratory Harbin China Aichi Cancer Center Hospital Dept. of Gastroenterological Surgery Nagoya Japan

ISBN: (纸本)9798350390995

Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based techniques can help with the task of classification. This research focuses on enhancing the process of lymph node classification in rectal cancer MRI images by using a multi-model approach. Firstly, we explore the use of diffusion models in data augmentation, to produce synthetic lymph node images to counter the issue of limited dataset size. Secondly, Real-ESRGAN and Swin2SR super-resolution techniques are examined for their ability to improve image quality and hence enhance the accuracy of lymph node classifications. Lastly, ConvNeXt is employed as an advanced technique used to improve overall performance at classifying lymph nodes for rectal cancer. Through a series of detailed experiments on the dataset consisting of T2-Weighted Axial MRI scans from 54 patients, we demonstrate the effectiveness of our approach in classification of lymph nodes. The metrics like accuracy and precision exhibit significant improvements, highlighting the superiority of our multi-model approach. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

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Multimedia Tools and Applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Twitter Bot For Consumer Feedback Analysis

Twitter Bot For Consumer Feedback Analysis

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Ashish Dwivedi Santosh Sharma Abhinav Mishra Ashutosh Sahu Himanshu Gupta Dept.Of Computer Science & Engineering United Institute of Technology Prayagraj India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

This research presents the development of a Twitter bot for consumer feedback analysis using natural language processing (NLP) techniques. The bot is designed to collect and analyze tweets related to specific products or services, and provide businesses with real-time insights on consumer sentiment and feedback. The bot uses a combination of NLP techniques, including sentiment analysis, topic modeling, and keyword extraction, to identify trends and insights from the tweets. The resulting analysis can be used by businesses to improve their products or services, as well as their customer engagement strategies. The paper also discusses the challenges and opportunities associated with developing and deploying such a bot, including ethical considerations related to privacy and data protection.

关键词： Training Analytical models Vocabulary Sentiment analysis Social networking (online) Blogs Chatbots

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