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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Khalid, Ayesha Adeeba, Farah Sehar, Najm Ul Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Department of Computer Science University of Engineering and Technology New Campus Pakistan

ISBN: (纸本)9798891762015

The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to higher Word Error Rates (WER). This paper presents the development of a large vocabulary Urdu ASR system for telephonic speech, based on a corpus of 445 speakers from diverse domains. The corpus, annotated at the sentence level, is used to train and evaluate GMM-HMM and chain Time-Delay Neural Network (TDNN) models on a 10-hour test set. Results show that the TDNN model outperforms GMM-HMM. Mixing narrowband and wideband speech further reduces WER. The test sets are also evaluated for the pre-trained model Whisper for performance comparison. Additionally, system adaptation for the banking domain with a specialized lexicon and language model demonstrates the system’s potential for domain-specific applications. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

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computers, Materials & Continua 2023年第12期77卷 2791-2811页

作者： Laila Almutairi Shabnam Mohamed Aslam Department of Computer Engineering Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Information Technology Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia

The rapid growth of service-oriented and cloud computing has created large-scale data centres *** data centres’operating costs mostly come from back-end cloud infrastructure and energy *** cloud computing,extensive communication resources are ***,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user *** is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching *** paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic *** primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data *** first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource ***-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed *** tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler *** EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关键词： EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation

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R3N2 : a novel approach for review based custom star rating using recurrent neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9089-9097页

作者： Kumar, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand Garhwal Uttarakhand Srinagar246174 India

Online shopping is growing in popularity as internet services are used more frequently. Many consumers consider online shopping due to the comfort, ease of access, and variety of products offered. However, this wide variety of products makes it hard for consumers to select one product from many available options. It is infeasible for any customer to go through many customer reviews given to the products. Therefore, the customers’ star rating becomes a viable solution for the star rating assigned to the products. However, the star rating-based system takes the mean of star ratings given by all the consumers. The mean value represents the masses’ view but fails to represent personal views. In this work, a novel star ratings-based system is proposed for the products to reflect the consumer’s individual needs and expectations by extracting customer reviews. A recurrent neural network is used as a sequential prediction model to hold aspect words and identify them after tokenization of a detailed review. The proposed R3N2 model enhances 0.9%, 2.5%, 4.9%, and 6.7% accuracy on digital products, electronics products, Kaggle movie reviews, and Apps datasets than the existing state-of-the-art (SOTA) models, respectively. Therefore, the proposed model outperforms SOTA models and may be used for real-time applications as it takes 2.56 ms per review. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sentiment analysis

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A Contemporary Framework for Detection of Phishing Website for Cyber Societal Safety

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SN computer Science 2025年第4期6卷 1-10页

作者： Lakshmi, B.N. Dhanalakshmi, B.K. Reddy, K. Tharun Department of Computer Science Engineering BMS Institute of Technology and Management Karnataka Yelahanka Bangalore 560064 India

In today’s digital age, phishing is a significant threat. It is a clever approach in which propagation of a website is done, and customers are enticed to a false website where customers are asked to enter critical personal information. Phishing website detection is clever and successful method that uses data mining algorithms to categories and associate websites. Detecting phishing websites is challenging since most of these methods are unable of making a dynamically right judgement about whether an encountered website is a phishing website or not. To determine efficiency, accuracy, the rules created, and speed, all rules and variables for grading phishing websites, also their relationships, were described and categorized. These algorithms are created. To identify phishing websites, we utilize a hybrid approach that includes developing description framework models for resource and categorizing websites using group learning algorithms. We utilize supervised learning methods to train our software. This strategy receives a high favorable rating, which is certainly noteworthy. We also used software to eliminate features that would allow us to estimate the frequency of each job in the dataset, a random forest classifier to cope with missing data sets. Our method will detect by utilizing the URL of website as input, helping us to have a good understanding of the factors that influence detection. The approach should be chosen as it increases accuracy in most situations. We get promising accuracy when our system investigates the strength of the Random Forest Algorithm and ensemble learning techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber safety KNN Learning techniques Random forest algorithm Society SVM Web phishing

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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Multi-Scale Spiking Pyramid Wireless Communication Framework for Food Recognition

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IEEE Transactions on Multimedia 2025年 27卷 2734-2746页

作者： Li, Wenrui Li, Jiahui Ma, Mengyao Hong, Xiaopeng Fan, Xiaopeng Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China Wireless Technology Lab Shenzhen518129 China

Food recognition applications in human health have recently garnered significant attention in the field of computer vision. With the advancement of mobile devices, robust food recognition in wireless communication has become a practical and challenging application scenario. We propose a novel Multi-scale Spiking Pyramid Transmission Network (MSPTN) to tackle this challenge. The MSPTN learns diverse and complementary local and global feature maps simultaneously, generating a comprehensive description of food images that capture the correlations of feed-specific features. The feature sender uses a three-layer Spiking Neural Network (SNN). The proposed sender compresses features into sparse and discrete spike trains, significantly reducing the required transmission bandwidth and improving channel utilization and energy efficiency. Our model introduces the Compressed Factorized Bilinear block (CFB), which employs a low-rank feature approximation to reduce computational complexity and feature transmission volume while preserving the discriminate features. The enhancement reasoning module is proposed to enhance the received features by projecting them into a higher-dimensional space and utilizing the self-attention mechanism and sum pooling to compress them back to the original dimension. We conduct extensive experiments on the ETH Food-101 and Food2k datasets. Our results reveal that the MSPTN demonstrates state-of-the-art recognition performance, even with binary spike trains. Meanwhile, the MSPTN also exhibits remarkable robustness in wireless communication scenarios. With the combination of CFB, SNN, and EFB, our model achieves significant efficiency gains, including a nearly nine-fold decrease in feature transmission volume and a three-fold improvement in runtime & computational memory speed. © 1999-2012 IEEE.

关键词： Bandwidth

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A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigation of sustainable strategies with metaheuristic algorithm on surface roughness, cutting temperature, and chip morphology during machining of Ti6Al4V alloy

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International Journal of Machining and Machinability of Materials 2024年第3期26卷 223-243页

作者： Misra, Sutanu Kumar, Yogesh Paul, Goutam Pradhan, Buddhadeb Department of Mechanical Engineering University of Engineering and Management Kolkata India Department of Mechanical Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cutting-edge temperature, and chip morphology to understand the machinability of Ti-6Al-4V alloy using sustainable manufacturing techniques. Here, a metaheuristic algorithm approach has been introduced to determine the experimental data’s accuracy, which is approximately 75% for further investigation. Logistic regression and support vector machine provided higher accuracy and best fitted with the dataset and problem formulation. During this work, it has been observed that chip morphology is influenced by cutting parameters and lubrication strategy. The cryogenic plus MQL hybrid approach is the best method over cryogenic cooling by decreasing the heat by 12% and giving 8% better surface quality. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Titanium alloys

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Deep learning-based real-world object detection and improved anomaly detection for surveillance videos

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Materials Today: Proceedings 2023年 80卷 2911-2916页

作者： Ragedhaksha Darshini Shahil Arunnehru, J. Computer Science Department SRM Institute of Science and Technology India

In this fast processing world, we need fast processing programs with maximum accuracy. This can be achieved when computer vision is connected with optimized deep learning models and neural networks. The goal of this project is to build an Artificial Intelligent system that will take live CCTV camera feed as input and detect what is happening in the video and do further analysis. Concerning current technology, there are a lot many models which use computer vision, machine learning for image and video processing. All models are different from each other, use various libraries, and are difficult to integrate or need high-end systems to process. This paper aims to use a convolutional neural network model for video processing and solve most of the important video processing features like detection of the liveliness of objects, estimating counts, and anomaly detection. And also further deploy it in such a way that it'll be easy to integrate and easy to use with API calls. © 2021

关键词： computer vision

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DSF-GAM: a location inference model in social network Twitter

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International Journal of computers and Applications 2025年第2期47卷 141-161页

作者： Paul, Amit Changder, Narayan Dutta, Animesh Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India TCG CREST Research and Education in Science and Technology West Bengal Kolkata India

Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds applications in various domains, such as targeted advertising and public health monitoring. Inference techniques through social interactions, such as co-mentions, remain a favored method for constructing user social networks. Co-mentions, however, introduce a substantial number of noisy relationships that impact the overall network structure and prediction of the location of a user. Furthermore, previous approaches have overlooked the examination of cohesive groups or community structures within an individual's ego network for location prediction. This paper introduces a predictive model called DSF-GAM (DS stand for document similarity, F stands for frequencies and GAM stands for Generalized Additive Model). which redesigns social interactions based on mentions to infer user location on Twitter. DSF-GAM introduces a novel and innovative social graph, termed a user relation graph. DSF-GAM employs a generalized additive model (GAM) for location inference. The fundamental contribution of DSF-GAM lies in the representation of data using a new graph structure with enriched community features and edges. The experimental results on well-known datasets demonstrate that DSF-GAM outperforms other state-of-the-art methods based on both content and network approaches and mixed results in hybrid approaches. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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