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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Parse, Tejas Ajay Awasthi, Tanishq Yadav, Dushyant Joshi, Piyush Indian Institute of Information Technology Department of Computer Science Sri City Chittoor India

ISBN: (纸本)9798350308433

Image denoising is a fundamental task in computer vision and image processing, crucial for improving the visual quality and interpretability of images captured in noisy environments. In this research, we propose a quality-Aware adaptive denoising (QAAD) approach that leverages the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) as a key factor in determining the optimal denoising algorithm for an image. Our methodology involves an initial assessment of image quality using BRISQUE scores for a diverse set of images. Subsequently, we employ various image quality enhancement algorithms to enhance the image quality. Through extensive experimentation, we assess the performance of these enhancement algorithms with respect to specific ranges of BRISQUE scores. This evaluation enables us to determine which denoising algorithm performs optimally for images falling within particular BRISQUE score ranges. The innovation in our approach lies in its adaptability. When an image proceeds for denoising, its BRISQUE score is calculated and, based on this score, the most suitable denoising algorithm for that specific image is selected. This adaptive strategy ensures that the chosen denoising algorithm is tailored to the inherent noise characteristics of the input image, leading to superior denoising results. Our experimental results demonstrate the effectiveness of our approach in enhancing image quality and achieving state-of-The-Art denoising results. This research contributes to the field of image processing by providing a data-driven and adaptive solution for image denoising, ultimately enhancing the quality of images in diverse real-world applications. © 2024 IEEE.

关键词： Image denoising

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Desktop Voice Assistant for Elderly People using Feed-forward Neural Network for Intent Recognition 2024

Desktop Voice Assistant for Elderly People using Feed-forwar...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Garg, Kashish Alam, Taj Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

technology has deeply embedded itself in our daily lives, making it easier to carry out tasks. However, the use of technology can be complicated for some like elderly people, cognitively and visually impaired individuals, illiterate, or people with limited education. A proposed desktop voice assistant for elderly people using a feed-forward neural network for intent recognition to simplify complex tasks and make technology more accessible for those who find it difficult. The assistant is a companion to assist, automate, and make it easier for older people to use computers. It aids them in understanding, responding to user needs, and assisting with task management and data retrieval. The Feed-forward Neural Network trains the pre-processed data for voice-to-intent recognition. The proposed and developed desktop assistant does voice recognition with an accuracy of around 78.5 percent, external APIs are used to enhance user experience simplify interactions, and boost productivity. The assistant's configurable personality, which is tailored to the user's preferences, makes it stand out. It provides a high level of interactivity, enabling it to be used quickly and is very flexible regarding individual requirements. © 2024 Copyright held by the owner/author(s).

关键词： Feedforward neural networks

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An Improved Intrusion Detection system for securing academic information on the cloud using a blockchain-based security framework 2

An Improved Intrusion Detection system for securing academic...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dubey, Krishna Bihari Goyal, Mukta Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

The concept of cloud computing has hastily grown in reputation, allowing businesses to shop and get access to facts from remote servers. Higher educational establishments have additionally adopted cloud services for storing and dealing with academic statistics. But this additionally poses a capability chance to the security and privacy of touchy facts. To deal with those challenges, we advise an advanced intrusion detection system (IDS) to secure the cloud's academic data. This ID uses a blockchain-based total safety framework to ensure the safety and integrity of stored statistics. The proposed IDS utilizes community visitors' monitoring and analysis to stumble upon anomalies and potential protection breaches. It also employs machine-learning techniques like anomaly detection algorithms to identify suspicious activities deviating from everyday behaviour styles. In the event of a detected intrusion, the device triggers an alert to the responsible parties and makes essential movements to mitigate the assault. The combination of blockchain technology offers an additional layer of security by developing an immutable and decentralized ledger to report all data transactions. This guarantees the integrity and authenticity of the academic statistics stored on the cloud. In case of any modifications or changes to the records, the blockchain will record them, creating a transparent audit path for accountability. Furthermore, the proposed IDS also consists of a belief management gadget that evaluates cloud carrier providers' popularity and protection posture. © 2024 IEEE.

关键词： Authenticity Cloud Community Decentralized Detected

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A hybrid convolutional neural network for sarcasm detection from multilingual social media posts

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Multimedia Tools and Applications 2025年第16期84卷 15867-15895页

作者： Pandey, Rajnish Kumar, Abhinav Singh, Jyoti Prakash Tripathi, Sudhakar Department of Information Technology IET Dr Rammanohar Lohia Awadh University Ayodhya Uttar Pradesh Faizabad India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj211004 India Department of Computer Science and Engineering National Institute of Technology Patna Patna India Department of Information Technology Rajkiya Engineering College Uttar Pradesh Ambedkarnagar India

Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments from informal multilingual comments about events or individuals, especially when the message is in a multilingual form. The prevalence of noisy datasets further complicates this task. This work proposes a hybrid convolutional neural network (CNN-H) model to classify sarcastic and non-sarcastic statements. Unlike traditional CNN models, CNN-H integrates character and word embeddings, enhancing its capacity to handle the complexities of multilingual content. Evaluation on five benchmark datasets, including English (news headlines, Ghosh Tweet, Riloff Tweet), Hindi, and multilingual Hindi-English (Hinglish), demonstrates the superior performance of CNN-H. The model surpasses various state-of-the-art conventional machine-learning and deep-learning models, including CNN, achieving an impressive F1-score of 93.0% on the multilingual dataset. The incorporation of character and word embeddings proves pivotal in mitigating the impact of noisy datasets, prevalent in social media posts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Centrality of Any Node in Social Network Analysis 2024

Centrality of Any Node in Social Network Analysis

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16th International Conference on Contemporary Computing, IC3 2024

作者： Srivastava, Milli Manju, Manju Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

In the contemporary landscape of advanced mobile technology, social media platforms have emerged as prominent arenas for expression, fostering vast volumes of communication ripe for research and analysis. Social network analysis (SNA) stands as a pivotal tool in identifying influential individuals within microblogging platforms, notably Twitter, which serves as a catalyst for online dialogues, trends, and content virality. The applications of discerning influential users on Twitter are multifaceted, ranging from viral marketing and brand analysis to news dissemination, health awareness campaigns, political activism, and governance empowerment. In this study, we introduce a sustainable method, termed Weighted Correlated Influence (WCI), designed to capture the relative impact of both activity on a timeline and characteristics of online users that are trend-specific. Weighted Correlated Influence in social network analysis evaluates the significance of nodes by combining their connectivity and influence, considering both the weight and correlation of their relationships. This metric helps identify key influencers and the overall structure of the network, highlighting nodes with high influence and strong, correlated connections. Our methodology integrates profile activity with underlying network topology to assign influence scores to users, synthesizing their combined effect. To evaluate the efficacy of our proposed methodology, we focus on the Twitter trend #Coronavirus Pandemic and validate the results against another social media trend. Experimental findings demonstrate the superior performance of WCI over existing methods. © 2024 Copyright held by the owner/author(s).

关键词： Tweets

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Virtual Fitness Trainer using Artificial Intelligence 2024

Virtual Fitness Trainer using Artificial Intelligence

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16th International Conference on Contemporary Computing, IC3 2024

作者： Gurbuxani, Shrey Gupta, Lakshay Madan, Kapil Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

An AI wellness coach is a software program that guides users through physical health routines by leveraging MediaPipe, OpenCV, and Python features. The program uses OpenCV computer vision techniques to monitor user progress and provide feedback on procedure and frame. To prepare the video data and provide real-time assessments, MediaPipe is used. Additionally, the app makes use of machine learning algorithms to provide users with tailored wellness recommendations and promote adoption. When these advances are combined, clients have an incredibly intelligent and practical way to advance their physical fitness. © 2024 Copyright held by the owner/author(s).

关键词： Python

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Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

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Soft Computing 2025年第4期29卷 2003-2013页

作者： Li, Te Chen, Liqiong Sun, Huaiying Hou, Mengxia Lei, Yunjie Zhi, Kaiwen School of Computer and Engineering East China University of Science and Technology Meilong road Shanghai200237 China School of Computer Science and Information Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China School of Chemistry and Environmental Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China

With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the temporal attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes an LSTM-DNN model based on the novel bidirectional Dynamic Time Warping (DTW) algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a novel bidirectional DTW algorithm to calculate the similarity between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" and "exploration" of information, and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs an Long Short-Term Memory-Deep Neural Networks (LSTM-DNN) to dynamically obtain user interests and preferences, and proposes a new metric Cumulative Self-System Diversity (CSSD) to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

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Performance Analysis of 2D Feature Descriptors for Object Classification

Performance Analysis of 2D Feature Descriptors for Object Cl...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Sah, Dhrupad Sankar, Prasad Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science Chittoor India

ISBN: (纸本)9798350376876

Image descriptors play a pivotal role in image and video processing by furnishing precise descriptions of local image characteristics. These descriptors typically exhibit invariance to rotation, translation, and scaling, and in some cases, demonstrate partial invariance to illumination alterations and affine or 3D projection transformations. Unlike contemporary Machine and Deep Learning methodologies, they do not necessitate extensive datasets or substantial computational resources for application, as they operate without the need for training. Key interest points, known as keypoints, are located within images, each associated with its distinctive feature descriptor. These descriptors facilitate the comparison of features across different images, aiding in the identification of shared objects. In literature, image descriptors have primarily served in tasks such as feature matching and scene object detection. This paper aims to investigate the potential of leveraging feature descriptors for image classification tasks and to gauge their comparative performance. Specifically, we employ Support Vector Machine (SVM) and Convolutional Neural Network (CNN) classifiers to train on feature vectors extracted from images, thereby learning the local features of depicted objects. To the best of our knowledge, our study represents the pioneering effort to scrutinize the performance of top 2D descriptors for image classification. We conducted a series of experiments using widely recognized image descriptors and datasets to evaluate and compare their performances. Our objective was to ascertain the reliability of these feature descriptors for image classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Analytical study of the encoder-decoder models for ultrasound image segmentation

Analytical study of the encoder-decoder models for ultrasoun...

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作者： Srivastava, Somya Vidyarthi, Ankit Jain, Shikha Department of Computer Science Engineering & Information Technology Jaypee Institute of Information Technology Noida India

Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ’s visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U2Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U2-Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound ne

关键词： Deep neural networks

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Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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