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SEOE:an option graph based semantically embedding method for prenatal depression detection

Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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A Novel Mixed Precision Distributed TPU GAN for Accelerated Learning Curve

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computer Systems science & engineering 2023年第7期46卷 563-578页

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Deep neural networks are gaining importance and popularity in applications and *** to the enormous number of learnable parameters and datasets,the training of neural networks is computationally *** and distributed computation-based strategies are used to accelerate this training *** Adversarial Networks(GAN)are a recent technological achievement in deep *** generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous ***,a GAN is trained on a single *** deep learning accelerator designs are challenged by the unique properties of GAN,like the enormous computation stages with non-traditional convolution *** work addresses the issue of distributing GANs so that they can train on datasets distributed over many TPUs(Tensor Processing Unit).Distributed learning training accelerates the learning process and decreases computation *** this paper,the Generative Adversarial Network is accelerated using the distributed multi-core TPU in distributed data-parallel synchronous *** adequate acceleration of the GAN network,the data parallel SGD(Stochastic Gradient Descent)model is implemented in multi-core TPU using distributed TensorFlow with mixed precision,bfloat16,and XLA(Accelerated Linear Algebra).The study was conducted on the MNIST dataset for varying batch sizes from 64 to 512 for 30 epochs in distributed SGD in TPU v3 with 128×128 systolic *** extensive batch technique is implemented in bfloat16 to decrease the storage cost and speed up floating-point *** accelerated learning curve for the generator and discriminator network is *** training time was reduced by 79%by varying the batch size from 64 to 512 in multi-core TPU.

关键词： Data parallel distributed model generative model learning curve mixed precision

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

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computer Systems science & engineering 2023年第5期45卷 1439-1454页

作者： Sujithra Kanmani Surendiran Balasubramanian Department of Computer Science and Engineering National Institute of Technology PuducherryKaraikalIndia

Online reviews significantly influence decision-making in many aspects of *** integrity of internet evaluations is crucial for both consumers and *** concern necessitates the development of effective fake review detection *** goal of this study is to identify fraudulent text reviews.A comparison is made on shill reviews *** reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion *** analyze textual features accessible in internet reviews by merging sentiment mining approaches with ***,the research improves fake review screening by using various transformer models such as Bidirectional Encoder Representation from Transformers(BERT),Robustly Optimized BERT(Roberta),XLNET(Transformer-XL)and XLM-Roberta(Cross-lingual Language model–Roberta).This proposed research extracts and classifies features from product reviews to increase the effectiveness of review *** evidenced by the investigation,the application of transformer models improves the performance of spam review filtering when related to existing machine learning and deep learning models.

关键词： Fraudulent sentiment readability BERT XLNET roberta XLMroberta

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Enhancing SDN Traffic Analysis Through Machine Learning on Preprocessed Controller Flow Statistics and Packet Analysis Data

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SN computer science 2025年第5期6卷 1-22页

作者： Vadivu, D. Sendil Santhosh, Ashwin Rajagopalan, Narendran Pavagada, Kaustub Valsaraj, Aswin Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

As an emerging technology, Software Defined Networks (SDN) has led to several vulnerabilities and risks, making it adoption challenging. Cyber threats in SDN include a wide range of malicious activities intended to exploit the architecture's centralized control and programming ability vulnerabilities. Adequate safety precautions for maintaining the stability of networks are required. Establishing an Intrusion Detection System (IDS) is vital in solving such issues, enabling real-time monitoring and classification of suspicious activity. Very few SDN-based datasets are available for attack detection. Hence, this study aims to generate two SDN-specific datasets, CSOR_SDN (Controller Statistics with Oftcl_Rest application for SDN) and WSCF_SDN (Wireshark Statistics with CicFlowmeter for SDN), from the network environment emulated using the Mininet and RYU controller by utilizing controller statistics and packet analysis data. The dataset generated with normal, DDoS (Distributed Denial of Service), DoS (Denial of Service), and probe attack flows is evaluated with multiple feature selection methods and Machine Learning (ML) classifiers to assess the complexity of the dataset. Correlation Coefficient-based feature selection (CFS) of filter methods is selected out of others with a Decision Tree (DT) classifier, capable of producing 100% results for the CSOR_SDN dataset with the least execution time in attack detection. For the proposed IDS, tenfold stratified cross-validation is performed with confidence interval assessment for the test dataset created on an enterprise network use case to highlight the scalability support of the model. A comparative analysis of both the dataset and its resource utilization is conducted to evaluate its feasibility. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber threat Dataset OpenFlow Ryu SDN Security Traffic analysis

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A self-organized priority-based MAC protocol in wireless sensor networks based on SDR-RHSO optimal relay node selection and HL-ANN wake-up scheduling

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11093-11102页

作者： Thalagondapati, Veeraiah Singh, Maheshwari Prasad Computer Science and Engineering Mahindra University Telangana Hyderabad India Computer Science and Engineering National Institute of Technology-Patna Bihar Patna India

A budding technology that possesses an extensive range of potential applications comprising environmental monitoring, medical systems, smart spaces, along with robotic exploration is a wireless sensor network (WSN). Medium Access Control (MAC) is a vital technique that evades collisions such that two interfering nodes do not transfer simultaneously for the WSN’s successful operation. However, owing to high energy consumption (EC), non-scalability, Delay, etc., designing an effective MAC protocol for WSN is a complex task. An efficient self-organized MAC protocol in WSN is formed by the work for conquering the issue. Utilizing Linear Scaling centered Social Spider Optimization Algorithm (LS-SSOA), the work is initiated with optimal cluster head (CH) selection under the Mahalanobis Distance centered K-Means Clustering (MD-KMC) technique, which extends the network lifetime (NLT) along with the stability of WSN. Afterward, an optimal relay node (RN) selection is created using Swap Displacement and Reverse-Based Rock Hyraxes Swarm Optimization (SDR-RHSO) for minimizing the energy loss throughout data transmission from sensor node (SN) to CH. Lastly, by identifying the active nodes and non-active nodes using Hebbian Learning-centered Artificial Neural Network (HL-ANN), a wake-up scheduling is performed betwixt the nodes that decrease the extra EC along with delay. A better network lifetime, lower energy consumption, higher throughput value, and accurate wake-up scheduling is attained by the work as exhibited by the experimental analysis and stays effective as analogized to the existing top-notch method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Scheduling

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Contemporary Approaches to Analyze Non-stationary Time-series: Some Solutions and Challenges

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Recent Advances in computer science and Communications 2023年第2期16卷 61-80页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Enhancement of technology yields more complex time-dependent outcomes for better understanding and analysis. These outcomes generate more complex, unstable, and high-dimensional data from non-stationary environments. Hence, more challenges are arising day by day to fulfill the increasing demand for future estimation. Thus, in this paper, an extensive study has been presented to comprehend the statistical complexity and randomness of Non-Stationary Time Series (NS-TS) data at the atomic level. This survey briefly explains the basic principles and terms related to Non-Stationary Time Series (NS-TS). After understanding the fundamentals of NS-TS, this survey categorized non-stationary time series into groups and subgroups based on a change in statistical behavior. It also provides a comprehensive discussion on contemporary approaches proposed by researchers in each category of non-stationarity. These algorithms include clustering, classification, and regression techniques to deal with different types of domains. Every category of non-stationarity consists of a separate table to draw some advantages and disadvantages of existing approaches. At the end of each non-stationarity type, a short discussion and critical analysis have been done. In the conclusion section, it is observed that this research sphere still has many open challenges that need to be addressed and demand more exploration. Furthermore, it discusses the possible solution of improvisation in future research. © 2023 Bentham science Publishers.

关键词： Time series

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Enhancing Malware Detection: A Comparative Analysis of Ensemble Learning Approaches 1

Enhancing Malware Detection: A Comparative Analysis of Ensem...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Ajvad Haneef, K. Shashank, N.S. Madhu Kumar, S.D. National Institute of Technology Department of Computer Science & Engineering Calicut India National Institute of Technology Department of Computer Science & Engineering Calicut India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392111

Malware represents the greatest threat to cybersecurity and serves as the weapon of choice for carrying out nefarious activities in the digital realm. The proliferation of malware poses significant risks to individuals and organizations. Therefore, malware detection is essential for companies to strengthen their cyberspace, protect sensitive data and minimize financial losses from cyberattacks. Ensemble learning methods have emerged as powerful tools in this domain, leveraging the strengths of multiple models to enhance detection accuracy and reliability. We present a comparative analysis of ensemble learning approaches for malware detection using the EMBER dataset. We evaluate six ensemble models, including Random Forest, AdaBoost, Extra Trees, XGBoost, LightGBM, and Bagging, based on key performance metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that all ensemble models demonstrate strong performance in malware detection, with Bagging ensemble achieving the highest accuracy and overall performance. The results highlight the effectiveness of ensemble learning techniques in enhancing malware detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Synergistic surface restructuring and cation mixing via ultrafast Joule heating enhancing ultrahigh-nickel cathodes for advanced lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 371-382页

作者： Haoyu Wang Jinyang Dong Meng Wang Yun Lu Hongyun Zhang Jinzhong Liu Yun Liu Na Liu Ning Li Qing Huang Feng Wu Yuefeng Su Lai Chen School of Materials Science and Engineering Beijing Key Laboratory of Environmental Science and EngineeringBeijing Institute of TechnologyBeijing 100081China Chongqing Innovation Center Beijing Institute of TechnologyChongqing 401120China Testing Centre of Power Batteries China North Vehicle Research InstituteBeijing 100072China

The implementation of ultrahigh-Ni cathodes in high-energy lithium-ion batteries(LIBs)is constrained by significant structural and interfacial degradation during *** this study,doping-induced surface restructuring in ultrahigh-nickel cathode materials is rapidly facilitated through an ultrafast Joule heating *** functional theory(DFT)calculations,synchrotron X-ray absorption spectroscopy(XAS),and single-particle force test confirmed the establishment of a stable crystal framework and lattice oxygen,which mitigated H2-H3 phase transitions and improved structural ***,the Sc doping process exhibits a pinning effect on the grain boundaries,as shown by scanning transmission electron microscopy(STEM),enhancing Li~+diffusion kinetics and decreasing mechanical strain during *** in situ development of a cation-mixing layer at grain boundaries also creates a robust cathode/electrolyte interphase,effectively reducing interfacial parasitic reactions and transition metal dissolution,as validated by STEM and time-of-flight secondary ion mass spectrometry(TOF-SIMS).These synergistic modifications reduce particle cracking and surface/interface degradation,leading to enhanced rate capability,structural integrity,and thermal ***,the optimized Sc-modified ultrahigh-Ni cathode(Sc-1)exhibits 93.99%capacity retention after 100 cycles at 1 C(25℃)and87.06%capacity retention after 100 cycles at 1 C(50℃),indicating excellent cycling and thermal *** presenting a one-step multifunctional modification approach,this research delivers an extensive analysis of the mechanisms governing the structure,microstructure,and interface properties of nickel-rich layered cathode materials(NCMs).These results underscore the potential of ultrahigh-Ni cathodes as viable candidates for advanced lithium-ion batteries(LIBs)in next-generation electric vehicles(EVs).

关键词： Lithium-ion batteries Ultrahigh-nickel layered cathodes In situ surface doping Cation mixing layer Structure and thermal stability

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