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检索条件"任意字段=4th International Conference on Computer Science and Network Technology, ICCSNT 2015"

共 8901 条记录，以下是1-10 订阅

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Proceedings of 2015 4th international conference on computer science and network technology, iccsnt 2015

Proceedings of 2015 4th International Conference on Computer...

引用

4th international conference on computer science and network technology, iccsnt 2015

ISBN: (纸本)9781467381727

the proceedings contain 327 papers. the topics discussed include: natural constant computed by Excel according to human method;an analysis system of infrared non-contact recognition of raw-milk gas-liquid two-phase flows;research on the simulation model of medical service pricing;development of a virtual training system for marine engineers;a direct data fetch technology applied in disaster-tolerant model of distributed file system;factory planning model of aircraft engine transmission parts based on the genetic annealing algorithm;analysis of electromagnetic scattering characteristics of target coated with finite-difference time-domain method;achieve high availability about point-single failures in OpenStack;a novel clustering and KWNN-based strategy for Wi-Fi fingerprint indoor localization;and classification of college students' mobile learning strategies based on principal component analysis and probabilistic neural network.

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A novel large kernel two-branch semantic segmentation network 24

A novel large kernel two-branch semantic segmentation networ...

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4th international conference on Signal Processing and Communication technology, SPCT 2024

作者： Zhang, Xinrui Fu, Peng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and multi-scale information fusion of features under different feeling fields. In this paper, in order to cope with this challenge, we propose to accumulate the receptive field in dense convolution using separable large kernel convolution through an adaptive semantic extraction module, and design a selectable large kernel attention module for the problem of mismatch of receptive field of feature information. Finally, this paper designs a multiscale feature extraction module that fuses local and global information, and uses large kernel dense separable convolution in the module with the ability to extract high-dimensional semantics serially and capture multiscale in parallel. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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A comprehensive review on detection of depression through social media analysis 4th

A comprehensive review on detection of depression through so...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Dogra, Shriya Kaur, Parneet Sharma, Tejpal Kumari, Nisha Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781032911571

Social media platforms like Instagram, Twitter, and Facebook have completely changed our world. People today exhibit a kind of digital character and are more linked than ever. While social media undoubtedly offers many noteworthy advantages, there are also unavoidable drawbacks. Later Research has demonstrated a link between elevated use of social media platforms and heightened levels of depression. the goal of the current study is to study the existing machine learning approaches to identify potentially sorrowful Twitter users based on their tweets and network behaviour. To accomplish this, we assessed classifiers that identify whether or not a person is depressed based on information extracted from their tweets and network behaviour. It is observed that as a greater number of features are incorporated, the F-score and accuracy in classifying depressed persons also improve. this technique is a predictive, data-driven tool for early mental health diagnosis of depression or any type of psychiatric ailments. the main contribution of the article is the examination of the traits and how they affect the ability to gauge depression severity. © 2025 the Author(s).

关键词： Tweets

来源：评论

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network intrusion detection system using random forest 4th

Network intrusion detection system using random forest

引用

4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Madhuri, P. David, D. Philomon, B. Reddy, K. K. Sripal Department of ECE Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781032911571

network Intrusion Detection Systems (NIDS) play a crucial role in safeguarding computer networks from unauthorized access and malicious activities. In this work, conducted a systematic examination of diverse machine learning techniques applied to NIDS with the aim of enhancing detection accuracy and efficiency. Comprehensively review and compare the performance of multiple algorithms, including but not limited to decision trees, neural networks, and ensemble methods, across various datasets and network environments. the findings of this work provide valuable insights into the suitability of different machine learning algorithms for network intrusion detection. By identifying the most effective techniques for specific types of intrusions and network scenarios. the work contributes to the ongoing efforts in developing robust and adaptive NIDS. In order to compare alternative intrusion detection approaches, researchers used the NSLKDD dataset, which contains 1,25,792 train samples and 22,544 test samples with 42 characteristics. In training and testing models, many algorithms such as Decision Tree, K-means clustering, Logistic Regression, MLP, and others are utilised. © 2025 the Author(s).

关键词： network intrusion

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Human intrusion detection by optimize dragon deep belief network 4th

Human intrusion detection by optimize dragon deep belief net...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Bohra, Garima Jha, C.K. Sharma, Neelam Department of Computer Science Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781032911571

Optimal weight selection in a novel model of Deep Belief Neural network (DBBN) is employed in this study to try to identify individuals in the video using a Dragonfly Algorithm (DA). With a learning rate of 0.001, the model’s Multilayer Perceptron (MLP) and Restricted Boltzmann Machine (RBM) layers were trained using Stochastic Gradient Descent (SGD) for every 50 epochs. Clean and representative data is essential for effective training;hence this procedure includes cleaning, preparing, segmenting, framing, preprocessing, and supplementing the data. With an accuracy rate of 91% on the UCF dataset, the DBBN model surpassed its rivals, the two-stream CNNs and the LSTM ensemble. An emphasis on systematically improving the model’s performance via tweaking its hyperparameters (such as activation functions, batch size, and epochs) will be detailed. Further development is needed to improve DBN models for security and surveillance activities, as demonstrated in this study. the primary areas for improvement include dealing with computational complexity and adapting to varied real-world settings. © 2025 the Author(s).

关键词： Stochastic systems

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Detection of plant diseases using image processing and machine learning 4th

Detection of plant diseases using image processing and machi...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Raj, Prateek Kumari, Akansha Sarkar, Sourin Kshitij Kumar, Prashant Verma, Anurag Parshotam School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9781032911571

With the global population steadily increasing, ensuring food security is a major concern, especially in agriculture. Detecting plant diseases early can make farming more efficient and prevent significant food losses. this paper proposes the development of cutting-edge technology tailored for farmers to detect plant diseases at an early stage. By leveraging ML and IP techniques, an accurate and effective algo is developed for identifying various diseases on leaves swiftly and accurately. the main focus is on utilizing machine learning approaches such as Support Vector Machine (SVM) and Convolutional Neural network (CNN), implemented in Python using libraries like Sklearn, TensorFlow, etc. this early detection system aims to facilitate timely intervention, ultimately minimizing food loss and contributing to global food security efforts. © 2025 the Author(s).

关键词： Adversarial machine learning

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network-based malware detection and classification by wavelet transformation and machine learning 4th

Network-based malware detection and classification by wavele...

引用

4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Arora, Mani Gupta, Anuj Kumar I.K.Gujral Punjab Technical University Jalandhar India CGC Landran Mohali India

ISBN: (纸本)9781032911571

In this era of the internet, malware poses a severe threat to cyber security and modern communication networks. It is very difficult to get protected from the exponential increase of malware threats and its variants with standard security solutions. there are various forms of malware and their variants that can easily compromise the network. Malware analysis, detection, and classification are a real-world tough problem for antivirus manufacturers, as they just rely upon signature-based solutions. thus, it is really important for detecting and classifying the various variants of malware that affect the network and computer system. the proposed approach uses textural features obtained from image-based malware samples using the wavelet decomposition method. Wavelet decomposition methods for analysing malware using wavelets, extracts the textural features obtained from the image of any malware sample. the feature vectors obtained from the proposed approach reduces the computational complexity and provides a malware detection rate of 98.88% on the benchmark dataset. the wavelet transformation approach for textural feature extraction of malware could be used in the detection of malware attacks in the network. It gives detection accuracy with a relatively small feature vector size. the efficacy and efficiency of the given experimental results depict 98.88% detection accuracy. © 2025 the Author(s).

关键词： Wavelet decomposition

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Facial expression recognition using Two-Stream Convolutional Neural networks (TSCNNs) and inception 3D Convolutional Neural network (CNN) 4th

Facial expression recognition using Two-Stream Convolutional...

引用

4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Kumar, Pramod Singh, Anuj Kumar Kumar, Manish Sabia Verma, Shorav Swami Rama Himalayan University Dehradun India Galgotias University Greater Noida India Chandigarh Engineering College CGC Landran Mohali India

ISBN: (纸本)9781032911571

Facial Expression Recognition (FER) is a hot topic in computer vision, thanks to its many uses in the fields including psychology, security, and human-computer interaction. In this paper, we present a method for face expression recognition using Inception 3D CNNs in conjunction with TSCNNs. A static picture stream and a facial sequences stream make up the structure. In order to record the spatial and temporal changes in facial expressions, it employs a three-dimensional convolutional neural network called Inception. the spatial properties from the image sequence stream can also be learned by the TSCNN architecture. this research applies the TSCNN architecture to picture sequences that are not provided by individual frames. the TSCNN architecture is used to process still images. the properties of both streams are combined using the late fusion approach. After testing the suggested framework with three publicly available datasets, the suggested method becomes clear. By comparing its outcomes to those of other state- of-the-art methods, the implemented approach proves to be more accurate. © 2025 the Author(s).

关键词： Convolutional neural networks

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. this method attains an equilibrium in the middle of security and efficiency. through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. these findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 IEEE.

关键词： Authentication Protocol

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Language identification using machine learning on social media text 4th

Language identification using machine learning on social med...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Raj, Rishav Ankita Dath, S. Sai Sholanki, Shivani Yadav, Anshuman Uchoi, Enjula Department of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9781032911571

One of the main areas researchers are looking into nowadays is how to change words from one language to another, known as transliteration. this is especially useful for cross-lingual applications where people need to translate words. Sometimes, people write in a mix of languages on social network platforms like X(twitter), Facebook, WhatsApp, which is called "mixed code language." To understand this kind of writing better, it’s important to figure out which language each word belongs to. In a recent study, a special type of deep learning model called Bidirectional Long Short-Term Memory (BLSTM) was introduced to analyze social media conversations in India. this model looks at the words that come before to figure out where each word comes from. Interestingly, the study found that using word embedding (a way of representing words) worked better than using character embedding. © 2025 the Author(s).

关键词： Adversarial machine learning

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