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6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate TB Lesion Segmentation and Classification

U-Net-Attention-TBNet: A Cutting-Edge Solution for Accurate ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： M. Saravana Karthikeyan J. Relin Francis Raj R. Parvathi S. Thanga Anushya R. Santhana Krishnan K. Paul Joshua Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SaveethaInstitute of Medical and Technical Sciences Chennai India Department of Smart Computing School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

U-Net-Attention-TBNet is an innovative deep-learning model developed for precisely segmenting and classifying tuberculosis (TB) lesions in chest X-ray images. This Model combines the U-Net architecture with an advanced attention mechanism, enhancing feature extraction and boosting detection accuracy. Trained on the CheXpert dataset, U-Net-Attention-TBNet demonstrates superior performance compared to existing models, including DenseNet with U-Net, ResNet with U-Net, and VGGNet with U-Net. It achieves significantly higher accuracy, precision, recall, and F1 score, showcasing its effectiveness in distinguishing between primary TB lesions, secondary TB lesions, miliary TB lesions, and calcified granulomas. The attention mechanism refines the Model's ability to focus on pertinent features, leading to improved segmentation and reduced false positives. This progress represents a significant advancement in TB diagnosis, offering enhanced reliability and efficiency in medical imaging and contributing to better management of tuberculosis.

关键词： Image segmentation Accuracy Attention mechanisms Tuberculosis Feature extraction Boosting Robustness Lesions X-ray imaging Biomedical imaging

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Detection and Analysis of Crop Stresses in Precision Agriculture Using Sentinel-2 Images

Detection and Analysis of Crop Stresses in Precision Agricul...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Gausiya Yasmeen Tasneem Ahmed Ajay Kumar Maurya Department of Computer Application Advanced Computing Research Laboratory Integral University Lucknow Uttar Pradesh India Department of Electronics and Communication Engineering National Institute of Technology Patna

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The ability to identify and monitor plant reactions to stress remotely has become a very challenging task for precision agriculture. Amongst the key factors in improving productivity of agriculture, one is, the identification of crop stresses. Therefore, relying just on visual inspection may lead to an inaccurate diagnosis, which would ultimately prevent the damaged plants from receiving corrective measures. Crop Stress can be abiotic like drought, flood, and salinity, while biotic stress includes weeds, other wild plants, and pests. In this paper, a focus has been laid to identify various causes of crop stress in the area located near the district of Gonda, situated in Uttar Pradesh state, India. For this purpose, Sentinel-2 images have been utilized to obtain the various vegetation indices such as NDVI, NDWI, GNDVI, PSSR, and Aphid Index, which are the most common to extract abiotic and biotic stress in crops.

关键词： Precision agriculture Visualization Image processing Salinity (geophysical) Crops Vegetation mapping Satellite images Reliability Stress Monitoring

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FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric

FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Met...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Albert H. Li Preston Culbertson Joel W. Burdick Aaron D. Ames Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA Department of Civil and Mechanical Engineering California Institute of Technology Pasadena CA USA

Many approaches to grasp synthesis optimize analytic quality metrics that measure grasp robustness based on finger placements and local surface geometry. However, generating feasible dexterous grasps by optimizing these metrics is slow, often taking minutes. To address this issue, this paper presents FRoGGeR: a method that quickly generates robust precision grasps using the min-weight metric, a novel, almost-everywhere differentiable approximation of the classical $\epsilon$ grasp metric. The min-weight metric is simple and interpretable, provides a reasonable measure of grasp robustness, and admits numerically efficient gradients for smooth optimization. We leverage these properties to rapidly synthesize collision-free robust grasps-typically in less than a second. FRoGGeR can refine the candidate grasps generated by other methods (heuristic, data-driven, etc.) and is compatible with many object representations (SDFs, meshes, etc.), We study FRoGGeR's performance on over 40 objects drawn from the YCB dataset, outperforming a competitive baseline in computation time, feasibility rate of grasp synthesis, and picking success in simulation. We conclude that FRoGGeR is fast: it has a median synthesis time of 0.834s over hundreds of experiments.

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A Comprehensive Analysis of Intrusion Detection in Internet of Things (IoT)

A Comprehensive Analysis of Intrusion Detection in Internet ...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Silpa Chalichalamala Niranjana Govindan Ramani Kasarapu Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computing Technologie SRM Institute of Science and Technology Kattankulathur Chennai India School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

The intrusion detection systems (IDS) play a vital role in both identifying malicious activity and enhancing network security. An IDS must be introduced to mitigate and identify hostile attacks within networks. In recent years, IDS has become a critical issue in the cyber security of Machine Learning, and Deep Learning techniques have been applied to IDS to increase their efficiency and accuracy. In this paper, several methods such as Stacked Contractive Autoencoder with Support Vector Machine, Convolutional Neural Network with Bidirectional Long Short-Term Memory, Particle Swarm Optimization with Convolutional Neural Network, and Convolutional Neural Network are used in the feature extraction process. Second, the Trust-based Intrusion Detection and Classification System, Modified version of Growth Optimizer, Aquila Optimizer, and Transient Search Optimizer methods are used in this process for feature selection. Finally, K-means with Random Forest, Single Hidden Layer Feed-Forward Neural Network, and Decision Tress methods are utilized in the classification. Extensive evaluation and comparisons of the proposed approaches were conducted by utilizing public datasets from cloud and IoT settings. The methods employed yielded outstanding outcomes in terms of identifying previously undiscovered attacks with a high degree of precision.

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IOT & Machine Learning Models to Develop The Architecture For Assessment Of Security Threats.

IOT & Machine Learning Models to Develop The Architecture Fo...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Mohammed Masaad Alotibi Sivaram Rajeyyagari Omaia Mohammed Al-Omari Mofadal Alymani Planning and Digital Transformation Ministry of Health Riyadh Saudi Arabia Department of Computer Engineering College of Computing and Information Technology Shaqra Saudi Arabi Department of Information Systems College of Computing and Information Technology Shaqra Saudi Arabi

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The Internet of Things (IoT) and other recent advancements in communication and information technology have profoundly impacted and enhanced people’s quality of life. IoT systems are vulnerable to previously unanticipated cyber threats such as denial of service, jamming, phishing, obfuscations eavesdropping, spoofing and invasions because of the widespread availability and rising demand for connected devices. The emerging cyber-physical security problem is difficult to avoid and guard against using conventional methods. Securing IoT systems calls for robust, dynamic, and current security mechanisms. When addressing emerging security concerns in cyber-physical systems (CPS), machine learning (ML) technology is often regarded as the most cutting- edge and promising option. This literature review explains how IoTs are built, looks into the many threats they face, and discusses the most up-to-date efforts being made to ensure their security using machine learning. In addition, it covers some of the research obstacles that may arise in the future while trying to implement security measures in IoT infrastructure.

关键词： Phishing Bibliographies Machine learning Cyber-physical systems Information and communication technology Security Internet of Things

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A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields

A Data-Driven Approach based on Dynamic Mode Decomposition f...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Joshitha Ravishankar Sally Khaidem Mansi Sharma Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel approach to efficiently represent and encode dynamic light field data for display applications based on dynamic mode decomposition (DMD). Acquired images are firstly obtained through optimized coded aperture patterns for each temporal frame/camera viewpoint of a dynamic light field. The underlying spatial, angular, and temporal correlations are effectively exploited by a data-driven DMD on these acquired images arranged as time snapshots. Next, High Efficiency Video Coding (HEVC) removes redundancies in light field data, including intra-frame and inter-frame redundancies, while maintaining high reconstruction quality. The proposed scheme is the first of its kind to treat light field videos as mathematical dynamical systems, leverage on dynamic modes of acquired images, and gain flexible coding at various bitrates. Experimental results demonstrate our scheme’s superior compression efficiency and bitrate savings compared to the direct encoding of acquired images using HEVC codec.

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Research on Miniaturization Trend of ChatGPT technology Model

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Journal of Artificial Intelligence and technology 2023年第3期3卷 95-99页

作者： Xuemei Shi Xiaoguang Du Xiedong Song Computer College of Huainan Normal University HuainanChina College of Computing and Information Technologies National UniversityPhilippinesPhilippines Biological Engineering of Huainan Normal University HuainanChina Department of Electronic Information Yantai City Vocational College of Science and TechnologyYantaiChina

Miniaturization and micro-miniaturization are trends in technology models,such as *** trends have the potential to enhance the practicality and professionalism of the model,as well as making them more widely ***,more individuals and organizations can leverage these technologies,and their impacts can be ***,miniaturization and micro-miniaturization can decrease the size of the model and the computing resources required,thus resulting the widespread use and development of artificial intelligence ***,they can boost the speed of model operation and training efficiency,thereby improving the practicality and efficacy of ***,this trend will have a profound impact on diverse fields,including scientific research,education,coaching,medical care,and daily life.

关键词： class ChatGPT model miniaturization practicality

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Facial Matching and Reconstruction Techniques in Identification of Missing Person Using Deep Learning

Facial Matching and Reconstruction Techniques in Identificat...

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India Council International Subsections Conference (INDISCON), IEEE

作者： R Annamalai Sudharson S Kolla Gnapika Sindhu Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai India Department of Computer Science and Engineering Vellore Institute of Technology Chennai India

The number of missing person cases has dramatically increased nowadays, leaving loved ones with a lot of unanswered questions. Police inquiries and public announcements are two regularly used traditional methods for locating missing persons, although they frequently fall short, especially over time. Artificial intelligence (AI) is gaining popularity and could be used to enhance the search process. This study offers a revolutionary approach for solving the unsolved cases of missing individuals by using AI-based facial matching and face reconstruction approaches. The proposed method successfully uses the ORL (Olivetti Research Laboratory) Dataset's Support Vector Machine (SVM) classifier to reach an outstanding accuracy of 93% by combining face landmarks and machine learning algorithms. Additionally, a 3D face reconstruction method based on Convolutional Neural Networks (CNN) trained on the varied 300-W dataset achieves a high accuracy of 90%. These results demonstrate the potential of AI and deep learning models for improving missing person identification. The proposed approach offers a viable option that aids in providing closure to the impacted families, making a significant contribution to the field and reducing crimes in the future.

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Cloud computing for Drug Discovery and Telemedicine in QSAR Analysis

Cloud Computing for Drug Discovery and Telemedicine in QSAR ...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Javed Ahmad Shahnawaz Ahmad Mohd Arif Mohd Nazim Department of CSE Sharda University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE School of Computing Science and Engineering Galgotias University Greater Noida India Department of CSE Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

This research is focused on developing drug discovery and telemedicine through advanced Quantitative Structure-Activity Relationship (QSAR) analysis. The performance of state-of-the-art machine learning (ML) algorithms like Random Forest (RF), DNN-QSAR, Support Vector Machine (SVM), was evaluated and the potential of Chemoinformatic based feature selection methods were explored for increasing the accuracy of developed QSAR models. However, not much innovation has been proposed in managing large chemical data and developing highly generalizable and more accurate QSAR models specific to telemedicine with low computational cost. Therefore, to fill this gap, we intended to use cloud computing (CC) as a downscale solution to host large chemical data and develop highly generalizable QSAR models that are more appropriate for telemedicine. This new way integrating cloud with QSAR for telemedicine is compatible with the world of current scientific research in pharmaceutical industries in which constant evolution and updating is a routine process. In addition to all these features integration of cloud also overcomes several inherited limitations of being non-performing with respect to speed up/QSAR analysis time or limited number or size chemicals database limitation that are used only in academic research studies.

关键词： Support vector machines Radio frequency Cloud computing Technological innovation Accuracy Telemedicine Computational modeling Data models Drug discovery Chemicals

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