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Sentimental Analysis of COVID-19 Twitter Data Using Machine Learning 8th

Sentimental Analysis of COVID-19 Twitter Data Using Machine ...

8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Likhith, S.R. Pooja Ahuja, S. Prathibha, B.N. Uma Shankari, B. Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India

ISBN: (纸本)9789819976218

Corona virus is considered as a scourge all over world by which most of the people got infected. Since the virus was spreading very quickly governments had to bring in very tough rules such as lock down. Lock down disrupted almost all sector like education, business etc.… During lock down and after the lock down people have used social media as their platform to express the opining regarding the pandemic. Tweeter is one such social media where people had shared their thoughts on the pandemic. In this study tweets which are made by the public is considered to analyses the sentiments. And in this research machine learning models are used to classify the data into five different classes namely positive, negative, neutral, extremely positive, and extremely negative. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes are the ML models used to classify the sentiment and are achieving on accuracy of 99%, 99%, 97%, 94%, and 73%, respectively. Further precision, recall, F1-score, and world clod are calculated for each and every class. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 Decision tree Logistic regression ML Naïve Bayes Random forest Sentiment analysis SVM Tweets

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LEO Satellite Oriented Wildfire Detection Model Using Deep Neural Networks: A Transfer Learning Based Approach 6

LEO Satellite Oriented Wildfire Detection Model Using Deep N...

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6th International Conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Gain, Mrityunjoy Raha, Avi Deb Biswas, Bristy Bairagi, Anupam Kumar Adhikary, Apurba Debnath, Rameswar Khulna University Computer Science and Engineering Discipline Khulna Bangladesh Ai Innovation Lab Khulna Bangladesh Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350385779

Wildfires are a recurring concern in several regions around the globe, often leading to catastrophic outcomes. The increasing accessibility of small satellites operating in Low Earth Orbit (LEO) has led to the potential for enhancing the speed of wildfire detection b y utilizing constellations of m any miniature satellites in the areas of interest. This research presents a system model for detecting and reporting wildfires to fire stations using LEO satellites. In this paper, we propose a transfer learning-based lightweight deep learning wildfire detection model at the base station to analyze pictures captured by LEO satellites and make predictions on the occurrence of wildfires. We use MobileNetV2 as our backbone model. MobileNetV2 is a lightweight model with low parameters. Lightweight models for image categorization offer faster inference, lower power consumption, efficient memory u sage, a nd ease of deployment, making them well-suited for real-time applications. However, a highly parameterized model can perform better than a poorly parameterized model. We apply transfer learning to ensure high performance in a low-parameterized model. The server must continuously check the images of all forest regions within a limited interval period to detect wildfires quickly. The lightweight model is very suitable for continuous image checking. Our experimental setup uses four baselines: VGG16, ResNet152, DenseNet161, and MobileNetv2. This study shows the efficacy of models in acquiring information from many sources within a given topic, as opposed to starting with no prior knowledge. We can also find from t he experimental results that our proposed low-parameterized transfer learning-based mobileNetV2 model achieves near 100 % accuracy, outperforming the other high-parameterized baselines. © 2024 IEEE.

关键词： Orbits

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Classification of Phishing Email Using Word Embedding and Machine Learning Techniques

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Journal of Cyber Security and Mobility 2022年第3期11卷 279-320页

作者： Somesha, M. Pais, Alwyn R. Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology karnataka Karnataka Surathkal575025 India

Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used. © 2022 River Publishers.

关键词： Embeddings

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECSCORE 31

Who Wrote This? The Key to Zero-Shot LLM-Generated Text Dete...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Junchao Zhan, Runzhe Wong, Derek F. Yang, B. Shu Liu, Xuebo Chao, Lidia S. Zhang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798891761964

The efficacy of detectors for texts generated by large language models (LLMs) substantially depends on the availability of large-scale training data. However, white-box zero-shot detectors, which require no such data, are limited by the accessibility of the source model of the LLM-generated text. In this paper, we propose a simple yet effective black-box zero-shot detection approach based on the observation that, from the perspective of LLMs, human-written texts typically contain more grammatical errors than LLM-generated texts. This approach involves calculating the Grammar Error Correction Score (GECSCORE) for the given text to differentiate between human-written and LLM-generated text. Experimental results show that our method outperforms current state-of-the-art (SOTA) zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts dataset. Additionally, our approach demonstrates strong reliability in the wild, exhibiting robust generalization and resistance to paraphrasing attacks. Data and code are available at: https://***/NLP2CT/GECScore. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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VMAP: Matching-based Efficient Offloading in IoT-Fog Environments with Variable Resources 17

VMAP: Matching-based Efficient Offloading in IoT-Fog Environ...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Morey, Jui Satpathy, Anurag Addya, Sourav Kanti Cloud and Smart System Services Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Missouri University of Science and Technology United States

ISBN: (纸本)9798350307672

Fog computing is a promising technology for critical, resource-intensive, and time-sensitive applications. In this regard, a significant challenge is generating an offloading solution that minimizes the latency, energy, and number of outages for a dense IoT-Fog environment. However, the existing solutions either focus on a single objective or mainly dedicate fixed-sized resources as virtual resource units (VRUs). Moreover, these solutions are restrictive and not comprehensive, resulting in poor performance. To overcome these challenges, this paper proposes a VMAP model addressing the lacunas above. Offloading problem is abstracted as a one-to-many matching game between two sets of entities - tasks and fog nodes (FNs) by considering both preferences. Moreover, the preferences and weights of the parameters are generated using the Analytic Hierarchy Process (AHP). Exhaustive simulations indicate that the proposed strategy outperforms the baseline algorithms, considering average task latency and energy consumption by 35% and 22.2%, respectively. Additionally, resource utilization also experiences a boost by 28.57%, and 97.98% of tasks complete their execution within the deadlines. © 2023 IEEE.

关键词： Fog

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Maximising Influence Spread in Complex Networks by Utilising Community-Based Driver Nodes as Seeds 1

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9th Annual International Conference on Information Management and Big Data, SIMBig 2022

作者： Sadaf, Abida Mathieson, Luke Bródka, Piotr Musial, Katarzyna Complex Adaptive Systems Lab School of Computer Science University of Technology Sydney Sydney Australia Department of Artificial Intelligence Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031354458

ISBN: (纸本)9783031354441

Finding a small subset of influential nodes to maximise influence spread in a complex network is an active area of research. Different methods have been proposed in the past to identify a set of seed nodes that can help achieve a faster spread of influence in the network. This paper combines driver node selection methods from the field of network control, with the divide-and-conquer approach of using community structure to guide the selection of candidate seed nodes from the driver nodes of the communities. The use of driver nodes in communities as seed nodes is a comparatively new idea. We identify communities of synthetic (i.e., Random, Small-World and Scale-Free) networks as well as twenty-two real-world social networks. Driver nodes from those communities are then ranked according to a range of common centrality measures. We compare the influence spreading power of these seed sets to the results of selecting driver nodes at a global level. We show that in both synthetic and real networks, exploiting community structure enhances the power of the resulting seed sets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

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