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A topical VAEGAN-IHMM approach for automatic story segmentation

Mathematical Biosciences and engineering 2024年第7期21卷 6608-6630页

作者： Yu, Jia Peng, Huiling Wang, Guoqiang Shi, Nianfeng School of Computer and Information Engineering Luoyang Institute of Science and Technology China Software Research Institute Technological University of Shannon Ireland

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. © 2024 the Author(s).

关键词： Hidden Markov models

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An Offline Biotic Stress Recognition Tool for Rice Plants Through Domain Shift

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SN computer Science 2024年第5期5卷 478页

作者： Pal, Chiranjit Chatterji, Sanjay Pratihar, Sanjoy Computer Science and Engineering Indian Institute of Information Technology Kalyani Kalyani 741235 India

Researchers are investigating deep learning techniques with their automatic feature learning capabilities for automated rice disease recognition from images. The current study has developed an ensamble model exploring hybrid features i.e., hand crafted and deep features. The proposed approach utilizes a laboratory image dataset comprising 2370 rice leaf images sourced from PlantVillage, augmented with another rice disease dataset featuring the same classes for training. These classes include BrownSpot, Leaf Blast, Hispa, and Healthy images in the dataset. The model achieves an accuracy of 97.9% through k-fold cross-validation. Considering the domain shift concept, we have tested the model’s accuracy on our real field rice leaf images containing BrownSpot, Leaf Blast, and Healthy leaves. The model achieves an accuracy of 93.7% on our dataset. To give the access of automatically identifying rice disease to the village farmers having poor internet connectivity, the current work introduces “easy to use” mobile application, RiceDiseaseRec. This research paves the way for automated rice disease recognition which leads to improving food security and mitigating crop yield losses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crop segmentation Deep learning Domain shift Rice disease detection

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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Complex Social Networks: Dynamics, Domains, and Dimensions 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Gera, Suruchi Sinha, Adwitiya Department of Computer Science and Engineering JSS Academy of Technical Education Noida India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

The online social platforms witnessed enormous growth in its networked structure as users continue to connect and interact through e-social dialogues. This eventually causes the transformational emergence of online social systems into complex networks. These large-scale networks inherently allow social entities to get engaged in diverse forms of interactions, thereby resulting in dynamic and complex user behavior. This raises many distinct challenges in social networks, which requires cross-dimensional analysis of the complex information for societal benefits. Understanding these complex systems has become crucial for analyzing the behavioral characteristics for drawing essential inferences. Analysis and evaluation of social complex networks in the context of several parameters are performed with prerequisites of graph theory, computational statistics, and probabilistic models. Our research provides a review of the evolving challenges in the social domain with case studies being conducted over multiple social systems. The study covers major aspects of social network theory that reveal communities and the impact of the central behavior of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique

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computer Systems Science & engineering 2023年第8期46卷 2589-2599页

作者： P.M.Siva Raja S.Brinthakumari K.Ramanan Computer Science and Engineering Amrita College of Engineering and TechnologyNagercoil629901India Information Technology K.C College of Engineering and Management Studies and ResearchThaneIndia Computer Science and Engineering NPR College of Engineering and TechnologyDindigul629902India

Brain tumor detection and division is a difficult tedious undertaking in clinical image *** it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great *** is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain *** have a difficult time sorting and classifying tumors from multiple *** tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are *** clinical characteristics are then retrieved and analyzed statistically to identify brain *** use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative *** dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture *** segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.

关键词： Brain tumor detection image segmentation nonlinear teager kaiser filtering

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Exploring the Impact of Financial News Sentiment on Stock Price Forecasting: A Comparative Deep Learning Approach 1

Exploring the Impact of Financial News Sentiment on Stock Pr...

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1st IEEE International Conference on Communications and computer Science, InCCCS 2024

作者： Rajhans, Abhishek Das, Trinanjan Kumar, Anish Sinha, Bam Bahadur Computer Science and Engineering Indian Institute of Information Technology Ranchi India Computer Science and Engineering National Institute of Technology Sikkim India

ISBN: (纸本)9798350358858

The application of Artificial Intelligence (AI) in stock price prediction has demonstrated significant advancements, with Machine Learning and Deep Learning techniques proving highly efficient in this domain. Two widely adopted architectures for stock price prediction are the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. Recognizing the potential impact of financial news sentiment on forecasting, this study investigates whether incorporating such sentiment yields superior results compared to relying solely on historical stock prices. Accurate predictions in the financial market are challenging due to its inherent high volatility and non-linear nature. To contribute insights into the effectiveness of various deep learning architectures, a comparative analysis was conducted on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and the Prophet model. The evaluation focused on three companies listed on the National Stock Exchange (NSE). The study employs the Mean Squared Error (MSE) metric to rigorously assess and compare the models’ performance. The results of the comparative analysis provide valuable implications for precision in stock price prediction within the context of market volatility and non-linearity. The study adds to the growing body of research on the application of AI in financial forecasting, emphasizing the importance of considering sentiment analysis alongside traditional approaches for enhanced predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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A voting-based trustworthy distributed IoT attack detection model

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Personal and Ubiquitous Computing 2025年第1期29卷 103-118页

作者： Sharma, Priya Sharma, Sanjay Kumar Dani, Diksha University School of Information & Communication Technology Gautam Buddha University Greater Noida India Department of Computer Engineering SVKM’s NMIMS Mukesh Patel School of Technology Management & Engineering Mumbai India

Besides the enhancement of the Internet of Things (IoT) distributed environment, anomalous activities are also escalating rapidly. Therefore, improving the trustworthiness of distributed networks is required for the extensive adoption of IoT infrastructure. Establishing a security mechanism in IoT networks is a challenging task as communication links are lossy and connected devices are resource-dependent. Conventional security techniques such as intrusion detection systems (IDS) are insufficient to shelter the IoT-distributed environment due to less computational capacity, restricted upgraded devices, and mismatched protocols. This paper proposes a novel machine learning-based trustworthy model for IoT attack detection. The proposed system combines the capability of Ada-boost and Gradient-boost to classify anomalous activities with low computational capacity proficiently and within a minimum time frame. Experiments were conducted on Distributed Smart Space Orchestration System (DS2OS) IoT dataset to assess the significance of the novel attack detection model. The demonstration shows that the proposed model obtains 98.28% training accuracy, 98.26% testing accuracy, and 99.25% AUC score. A confusion matrix has also been calculated to check the authenticity of the results—a comparison of the proposed model with individual learners and existing models proves that the proposed model outperformed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Network intrusion

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Joint Watermarking and Encryption for Social Image Sharing

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computers, Materials & Continua 2025年第5期83卷 2927-2946页

作者： Conghuan Ye Shenglong Tan Shi Li Jun Wang Qiankun Zuo Bing Xiong School of Information Engineering Hubei University of EconomicsWuhan430205China School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China

With the fast development of multimedia social platforms,content dissemination on social media platforms is becomingmore *** image sharing can also raise privacy *** encryption can protect social ***,most existing image protection methods cannot be applied to multimedia social platforms because of encryption in the spatial *** this work,the authors propose a secure social image-sharing method with watermarking/fingerprinting and ***,the fingerprint code with a hierarchical community structure is designed based on social network ***,discrete wavelet transform(DWT)from block discrete cosine transform(DCT)directly is *** that,all codeword segments are embedded into the LL,LH,and HL subbands,*** selected subbands are confused based on Game of Life(GoL),and then all subbands are diffused with singular value decomposition(SVD).Experimental results and security analysis demonstrate the security,invisibility,and robustness of our ***,the superiority of the technique is elaborated through comparison with some related image security *** solution not only performs the fast transformation from block DCT to one-level DWT but also protects users’privacy in multimedia social *** the proposed method,JPEG image secure sharing in multimedia social platforms can be ensured.

关键词： Multimedia security digital watermarking image encryption image sharing privacy protection

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