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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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An Adaptive and Scalable Framework for Resource-Efficient Deployment of Mixture of Experts in LLM-Based Intelligent IoT Networks

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IEEE Internet of Things Journal 2025年第13期12卷 22746-22757页

作者： Liu, Chengxu Li, Yangfan Chen, Cen Zou, Xiaofeng Kuang, HaiLan Ma, Xiaolin Lu, Zeyu Zhang, Zhaoyuan Liu, Jing Liu, Xinhua Central South University School of Computer Science and Engineering Changsha410083 China Wuhan University of Technology School of Information Engineering Wuhan430000 China South China University of Technology School of Future Technology Guangzhou510641 China Pazhou Laboratory Guangzhou510335 China School of Information Engineering of Wuhan University of Technology Wuhan430000 China Guilin University of Electronic Technology Computer and Information Security Department Guilin China Hunan Post and Telecommunication College China

The exponential growth of the Internet of Things (IoT) necessitates the deployment of large-scale models capable of processing the complex and diverse data generated by IoT devices. However, the substantial memory requirements of these models pose significant challenges, especially in scenarios where rapid decision-making and low-latency responses are critical. To address these challenges, we propose three innovative strategies for optimizing large model usage in IoT environments. The first strategy is an adaptive loading scheme, which enables dynamic loading of individual model experts. The second strategy involves an expert-by-expert loading approach, further enhancing the ability to load experts as needed, which optimizes memory usage and accelerates computations. The third strategy employs an inter-layer expert reuse mechanism, facilitating the efficient reuse of experts across different layers, thus enhancing response rates without compromising model accuracy. Importantly, these strategies can be directly applied to Mixture of Experts (MoE) large language models without requiring additional training, thereby providing a seamless and efficient solution for leveraging these models in memory-constrained, high-performance IoT environments. © 2014 IEEE.

关键词： Computational modeling Load modeling Adaptation models Memory management Loading Internet of Things Transformers Resource management Graphics processing units Decoding

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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TranSVPath: A TabTransformer-Based Model for Predicting the Pathogenicity of Structural Variants

TranSVPath: A TabTransformer-Based Model for Predicting the ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Yaning Wang, Jiawei Wang, Xiaoqi Xu, Liwen Pan, Liangrui Peng, Shaoliang Xiangtan University School of Computer Science Xiangtan China Hunan University College of Computer Science and Electronic Engineering Changsha China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350386226

Genomic structural variants are recognized as critical molecular factors contributing to various major diseases, including cancer and genetic disorders. However, accurately determining whether a variant can cause a disease in clinical settings is extremely challenging due to limited sample sizes, the diversity of variant types, and the complexity of the mechanisms linking variants to diseases. Existing computational tools attempt to predict the pathogenic effects of these variants, but they often consider only single-layer biological data, limiting their ability to comprehensively explain the functional impacts of the variants. To address this, we propose TranSVPath, a pathogenicity scoring tool for structural variants based on the Transformer framework. TranSVPath provides a more comprehensive biological annotation of structural variations by integrating multi-dimensional data, including overlap with specific genomic regions, single nucleotide variant deleteriousness scores, phylogenetic conservation scores, Mendelian clinical application pathogenicity scores, and gene function loss. Additionally, TranSVPath employs a variant of the Transformer architecture tailored to this integrated data. Its attention mechanism captures key biological features, facilitating molecular-level interpretation of the pathogenic mechanisms of variants. Consequently, TranSVPath can accurately predict the pathogenicity of deletions, insertions, tandem duplications, inversions, and microsatellite insertions. Evaluations on large-scale human structural variant datasets demonstrate that TranSVPath outperforms state-of-the-art algorithms across relevant metrics. © 2024 IEEE.

关键词： Pathogens

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Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks

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Soft Computing 2023年第5期27卷 2739-2750页

作者： Javadpour, Amir Rezaei, Samira Sangaiah, Arun Kumar Slowik, Adam Mahmoodi Khaniabadi, Shadi School of Computer Science and Technology Guangzhou University Guangzhou510006 China Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands School of Computing Science and Engineering VIT University Vellore India Department of Industrial Engineering and Management National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland Department of Computer Science and Engineering USM Engineering Campus Pulau Pinang George Town14300 Malaysia

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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A Construction of Object Detection Model for Acute Myeloid Leukemia

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Intelligent Automation & Soft Computing 2023年第4期36卷 543-560页

作者： K.Venkatesh S.Pasupathy S.P.Raja Department of Computer Science and Engineering Annamalai UniversityChidambaram608002India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was signiﬁcant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classiﬁ*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关键词： Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu

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Forecasting Cloud Workload through Reinforcement Learning-Based Ensemble Model 4

Forecasting Cloud Workload through Reinforcement Learning-Ba...

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4th International Conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Kang, Yuliang Li, Yongke Zhang, Gongxuan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331534622

The escalating demand for cloud computing has intensified the need for accurate workload forecasting to optimize resource allocation and maintain service quality. This paper presents a novel approach employing a reinforcement learning-based ensemble model to enhance the accuracy of workload predictions in cloud environments. The study addresses the challenges posed by complex and dynamic workload patterns, leveraging deep learning techniques to improve prediction outcomes. The proposed model dynamically selects the optimal subset of predictors, demonstrating superior performance on the Google Cluster dataset. The research aims to contribute to more efficient and intelligent resource management in cloud computing. © 2024 IEEE.

关键词： Prediction models

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Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

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Multimedia Tools and Applications 2024年 1-26页

作者： Singh, Ajay Dhanaraj, Rajesh Kumar Kumar, Santosh Abraham, Ajith School of Computer Science and Engineering Galgotias University Greater Noida India Pune India Department School of Computer Science Engineering & amp Technology Bennett University Uttar Pradesh Greater Noida India

Providing accurate and timely traffic information such as arriving time of train plays a significant part in intelligent train status prediction. Maximum-speed train status forecast is a significant topic as far as railway dispatching. In this paper proposes a novel train status prediction method based on the Hannan Quinn Quantum Grasshopper Optimized Attention Deep Learning (HQQGO-ADL) method introduced for accurate train status prediction. The proposed HQQGO-ADL is designed with three different processes namely preprocessing, feature selection, and prediction. This method can well capture the influence of discrete and continuous features in addition to spatiotemporal characteristics, therefore modeling train status prediction. At first, Mid-hinge Inter-Quartile Interpolation is utilized to perform preprocessing for eradicating or discarding the outliers with less train status identification time. Second, HannanQuinn Quantum Grasshopper Optimization is employed to capture the influence of discrete and continuous features on spatiotemporal characteristics, therefore modeling train status prediction. Besides, the maximum log likelihood discrete feature fine tuned to obtain continuous features. Finally, the Attention Deep Neural Train Status Prediction model is utilized for improved latency train status prediction. The overall analysis of HQQGO-ADL has better performance of higher train status prediction accuracy by 32% and lower train status prediction time, overhead, and error rate by 32%, 32%, and 27%as compared to existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Observe before Generate: Emotion-Cause aware Video Caption for Multimodal Emotion Cause Generation in Conversations 24

Observe before Generate: Emotion-Cause aware Video Caption f...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Fanfan Ma, Heqing Shen, Xiangqing Yu, Jianfei Xia, Rui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400706868

Emotion cause analysis has attracted increasing attention in recent years. However, the integration of multimodal information with emotion causes remains underexplored. Existing studies merely extract utterances from conversations as cause evidence, which is too coarse-grained to locate the exact causes from other modalities, especially those that may be reflected only in a specific video frame of an utterance. To address these limitations, we introduce a new task named Multimodal Emotion Cause Generation in Conversations (MECGC), which aims to generate an abstractive summary clearly and intuitively describing the causes that trigger the given emotion based on the multimodal context of conversations. We accordingly construct a dataset named ECGF that contains 1,374 conversations and 7,690 emotion instances from TV series. We further develop a generative framework that first generates emotion-cause aware video captions (Observe) and then facilitates the generation of emotion causes (Generate). The captioning model is trained with examples synthesized by a Multimodal Large Language Model (MLLM). Experimental results demonstrate the effectiveness of our framework and the significance of multimodal information for emotion cause analysis. Our dataset and source codes are available at https://***/NUSTM/MECGC. © 2024 ACM.

关键词： emotion cause analysis in conversations

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A review of explainable AI in medical imaging: implications and applications

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International Journal of computers and Applications 2024年第11期46卷 983-997页

作者： Kinger, Shakti Kulkarni, Vrushali Department of Computer Engineering and Technology WPU School of Computer Science & Engineering Dr. Vishwanath Karad MIT World Peace University Maharashtra India

Deep learning approaches have attained remarkable success across various artificial intelligence applications, spanning healthcare, finance, and autonomous vehicles, profoundly impacting human existence. However, their black-box nature, lack of transparency, and inability to elucidate conclusions have hindered their adoption in high-risk applications. Explainable Artificial Intelligence (XAI) encompasses a suite of tools, approaches, and algorithms aimed at furnishing highly accurate explanations while preserving robust accuracy. This research paper investigates the intricate and evolving domain of Explainable Artificial Intelligence (XAI), particularly its implications in healthcare, with the objective of fostering trust in AI utilization within this domain. It encompasses a spectrum of topics, including a compendium of tasks XAI should fulfil in medical imaging, an examination of current methodologies for yielding transparent and understandable outcomes in medical imaging, criteria for assessing AI system explainability, and recommendations for integrating XAI into medical imaging practices. This review facilitates the selection of suitable and efficient XAI techniques for medical imaging while aiding developers in grasping the fundamentals of these methodologies. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Medical imaging

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