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12th International Conference on Big Data and Artificial Intelligence, BDA 2024

作者： Navpreet Roul, Rajendra Kumar Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

ISBN: (纸本)9783031818202

Recently, surging urbanization and the subsequent increase in vehicular movement have amplified the popularity of parking management systems. A crucial hurdle in effective parking management is the optimal utilization of available parking spaces. Although deep learning techniques have showcased their prowess in accurately identifying parking spaces, they sometimes falter when partial obstructions and diverse lighting conditions are present. This challenge is further exacerbated during foggy or hazy weather, resulting in a decline in their performance. This research article introduces a novel approach that leverages the benefits of CNN and Kernel Extreme Learning Machine (KELM). The approach not only enhances accuracy but also significantly reduces training time. The key innovation lies in replacing the fully connected layer of CNN with KELM, which circumvents the backpropagation process and reduces the training time. A purpose-built data set tailored to classify parking spaces in hazy conditions and PKLot is employed for the validation. Within the proposed approach, images from the hazy dataset undergo preprocessing using Light-DehazeNet (LD-Net) to counteract the effects of haze. Subsequently, CNN is trained on this preprocessed dataset to extract features and generate a feature vector. This feature vector is input into the KELM to classify parking spaces as occupied or vacant. Compared with standalone CNN, KELM, machine learning classifiers, and similar existing models, the proposed model showcases superior performance and better computational efficiency. This study demonstrates that the integration of CNN with KELM offers a promising solution for parking space classification in challenging weather conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Unmasking offensive content: a multimodal approach with emotional understanding

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Gopendra Vikram Ghosh, Soumitra Firdaus, Mauajama Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801103 India Fondazione Bruno Kessler Povo Trento38123 Italy University of Alberta Edmonton Alberta Canada Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India

The widespread use of social media has brought people from all over the world closer, but it has also become a platform for spreading hate, offensive content, and misinformation. Various forms of media, such as text, images, audio, and video, are used to disseminate information. In this paper, we tackle the task of identifying offensive and non-offensive text in the context of women’s harassment, considering the intensity of the offense. We propose a novel approach that leverages sentiment and emotion, including their intensity, to enhance the performance of this task. To facilitate our research, we constructed a large-scale dataset called the Women and Child-related Sentiment and Emotion-Aware offensive dataset (WCSEoff) to aid in identifying offensive content. Furthermore, we develop a multi-task model that not only detects whether images contain offensive or non-offensive content and quantifies the intensity of offensiveness as the primary task but also classifies the sentiment and emotions present, along with their respective intensities, as the auxiliary task. By adopting this multitasking framework, we achieve a performance improvement of over 7% compared to approaches that do not incorporate sentiment and emotion analysis. Code and data will be available at https://***/~ai-nlp-ml/***#MOSAIC. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Classification (of information)

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Indian Sign Language Recognition in Real Time using YOLO NAS 3

Indian Sign Language Recognition in Real Time using YOLO NAS

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Raj, Rohit Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Anish, Mohammad Pune Institute of Computer Technology Department of E&TC Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350354171

Normal people typically communicate using voice, while deaf individuals use sign language to communicate with each other. However, a challenge arises when deaf people need to interact with those who do not understand sign language. To address this issue, we propose developing a model that can recognize Indian Sign Language and translate it into text. Deep learning approaches have been used into computer vision in recent years, greatly advancing the area of sign language identification and improving accessibility for the deaf. To achieve real-time recognition of Indian Sign Language, we have used YOLO NAS (You Only Look Once Neural Architecture Search) object detection *** YOLO NAS architecture achieves 0.9568 mean Average Precision (mAP) with intersection over union (IoU) threshold of 50% across 100 classes, using our selfcreated Sign Language dataset. © 2024 IEEE.

关键词： Object detection

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

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SN computer Science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Smart Waste Management 15

Smart Waste Management

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kanade, Dnyaneshwar More, Aniket Binnar, Nilesh Narnaware, Paridhi Narote, Atharv Patil, Niraj Vishwakarma Institute of Technology Department of Multidisciplinary Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798331300579

It is the responsibility of every individual to ensure the cleanliness and sustainability of the environment. We have created a waste management model that utilizes various sensors such as soil moisture sensors, ultrasonic sensors, and inductive proximity sensors, along with a conveyor belt, to automate the process of waste segregation. The ultrasonic sensor is used to detect dry waste based on its distance from the sensor, while the soil moisture sensor is employed to identify wet waste by measuring the moisture content in the waste. The inductive proximity sensor plays a crucial role in identifying metal waste by detecting any metallic objects present in the waste stream. The entire system is designed to streamline the waste separation process, thereby minimizing the need for manual sorting and enhancing overall efficiency. By integrating these sensors with the conveyor belt mechanism, the system enables real-time waste detection and segregation, thereby optimizing waste management operations. The proposed intelligent waste management system offers numerous advantages, including enhanced precision in waste categorization, reduced human errors, increased efficiency in waste collection, and the potential for recycling and resource recovery. © Grenze Scientific Society, 2024.

关键词： Soil moisture

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High Dynamic Range Imaging for Dynamic Scenes Based on Multi-Level Spike Camera

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5394-5406页

作者： Zhu, Zhenkun Xiong, Ruiqin Zhao, Jing Zhao, Rui Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking University School of Computer Science Beijing100871 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the current design is that each spike only represents the arrival of a fixed amount of photons. It can not deal with strong light areas in which the amount of accumulated photons reaches the pre-specified threshold multiple times within a single readout interval. In this paper, we propose a new spike camera model of high-speed imaging for high dynamic range scenarios. In this scheme, each pixel accumulates the incoming photons persistently and generates a new type of spike stream in which each spike symbol may be associated with different levels, indicating the arrival of different amounts of photons since the last readout. This enables the camera to support dynamic scenes with wider dynamic range. To achieve this, we propose a two-level buffer mechanism, one for photon accumulation and one for spike-firing encoding. We use a register to hold the number of spike-firings which has not been read out yet. At each readout time, the major part in the counter is read out via a carefully designed exponential encoding and the counter is updated. Such encoding and readout strategy enables a very efficient expansion of the dynamic range using a small number of encoding bits. Furthermore, we propose an image reconstruction scheme for the proposed camera, utilizing both spike intervals and spike levels to recover the light intensity. We incorporate Mamba and propose a temporal-spatial selective scan mechanism to extract temporal-spatial correlation within spike streams. We employ a pyramid adaptive filtering and alignment module to achieve coarse-to-fine feature alignment. Experimental results show that the proposed scheme can achieve better imaging quality and outperform the existing spike camera in high dynamic range scenarios. © 1991-2012 IEEE.

关键词： Adaptive filtering

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Cooperative Channel and Optimized Route Selection in Adhoc Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1547-1560页

作者： D.Manohari M.S.Kavitha K.Periyakaruppan B.Chellapraba Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119TamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035TamilnaduIndia Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107TamilnaduIndia Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is *** uses have expanded in lockstep with its *** to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is *** this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the conﬁdence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization *** that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated *** simulation work is validated in this section using the MATLAB ***,steps show how the node acts as a conﬁdent forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between *** cal-culate the conﬁdence of the node at the start of the network by combining the reliability report for theﬁrst hop and the reliability report for the secondary *** then refer to the same node as the conﬁdent node in order to operate as a *** a result,we witness an increase in the leftover energy in the *** percentage of data packets delivered has also increased.

关键词： Adhoc Network conﬁdent forwarder one-hop optimized route selection secondary report channel selection

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Concurrent and Learned Data Structures 25

Concurrent and Learned Data Structures

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Chatterjee, Bapi Peri, Sathya Department of Computer Science and Engineering IIIT Delhi Delhi India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798400710629

We explore the landscape of modern data structures that employ two powerful scaling mechanisms simultaneously: (a) concurrency to harness the power of today's multicore processors, and (b) learned queries - the operations employing machine learning inference by models trained on the underlying data. We focus on advancements, challenges, and potential future directions in this field. We delve into the motivation for incorporating learned queries in concurrent data structures. We discuss the key techniques, applications, and open research questions. On the theoretical side, we discuss the mathematical foundations for building the learned data structures in addition to the correctness of queries in a concurrent setting. On the practical side, we present an overview of techniques used for managing concurrency with learned queries that need training the models with concurrent real-time updates. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Two-Level Image Augmentation with Deep-Learning for Single Sample Face and Ear Recognition

Two-Level Image Augmentation with Deep-Learning for Single S...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Tomar, Vivek Kumar, Nitin Deshmukh, Maroti Singh, Maheep University Computer Science & Engineering Department Dehradun India Computer Science & Engineering Department Chandigarh India National Institute of Technology Computer Science & Engineering Department UttarakhandGarhwal Srinagar India National Institute of Technology Uttarakhand Doon University Computer Science Department Dehradun India

ISBN: (纸本)9798331540821

Single-sample face and ear recognition is a challenging sub-problem in biometric recognition, as only one sample is available for training. This article presents a method to recognise the subject from a single face or ear image with two-level image augmentation, followed by using a pre-trained deep learning model for feature extraction and classification. First, sampling is used for generating additional samples, and then mirroring and rotation are used. Pre-trained VGG16 model is used for parameter estimation and classification. The proposed method is tested on three face and two ear databases. The proposed method achieves classification accuracy of 84.83 %, 77.08 %, and 88.52 % on ORL, Yale, and FeedTum databases and 66.69% and 78.29% on AMI and IITD databases, respectively. The image quality of the generated samples is also found to be in an acceptable range. The proposed method can be helpful and effective when smaller databases and fewer training images are available. © 2024 IEEE.

关键词： Image quality

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