An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.

基于SimpliciTI协议的井下液压支架压力无线监测系统的研究与应用

针对目前煤矿井下液压支架压力有线监测系统布线复杂和数据传输可靠性低的问题,设计了一种基于SimpliciTI协议的井下液压支架压力无线监测系统。该系统以无线射频芯片CC1101为核心,结合SimpliciTI协议,对液压支架的压力数据进行采集、显示、存储、无线路由及发送等操作,实现了对无线覆盖的整个工作面的液压支架压力监测。

Multimodal SocialMedia Fake News Detection Based on Similarity Inference and Adversarial Networks

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

Complexity Behind Simplicity——An analysis of Langston Hughes' Art of Writing in "Early Autumn"

Love is an eternal subject with many references in many novels,but Langston Hughes approaches it in a most simplified manner to portray the complex feeling of the protagonists.He achieves this inward complexity through carefully-treated outward simplicity.The paper discusses this art of writing in Early Autumn from such aspects as the dramatic point of view,well-designed setting,careful presentation and effective rhetorical devices.

Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

有可变速度生长(LUHNM-VSG ) 的一个大统一混合网络模型作为统一混合网络的第三个模型被建议理论框架(UHNTF ) 。到随机的连接数字和可变速度生长索引的确定的连接数字的混合生长比率 vg 在它被介绍。vg 并且在 LUHNM-VSG 的拓扑的转变特征上的主要效果被揭示。为与另外的模型一起的比较，我们与七个层次，从底部，到金字塔简洁普遍性的最高的 level-7 的 level-1 在正在增加构造网络复杂性金字塔的一种类型但是复杂性差异正在减少。在他们之间的转变关系取决于四混合比率匹配(医生， fd， gr， vg ) 。因此，网络模型的大多数能经由四混合比率以统一方法被调查(医生， fd， gr， vg ) 。金字塔的 level-1 对对真实世界的网络的描述好一些、更靠近以及有潜在的申请的 LUHNM-VSG。

Fake News Detection Based on Multimodal Inputs

In view of the various adverse effects,fake news detection has become an extremely important task.So far,many detection methods have been proposed,but these methods still have some limitations.For example,only two independently encoded unimodal information are concatenated together,but not integrated with multimodal information to complete the complementary information,and to obtain the correlated information in the news content.This simple fusion approach may lead to the omission of some information and bring some interference to the model.To solve the above problems,this paper proposes the FakeNewsDetectionmodel based on BLIP(FNDB).First,the XLNet and VGG-19 based feature extractors are used to extract textual and visual feature representation respectively,and BLIP based multimodal feature extractor to obtain multimodal feature representation in news content.Then,the feature fusion layer will fuse these features with the help of the cross-modal attention module to promote various modal feature representations for information complementation.The fake news detector uses these fused features to identify the input content,and finally complete fake news detection.Based on this design,FNDB can extract as much information as possible from the news content and fuse the information between multiple modalities effectively.The fake news detector in the FNDB can also learn more information to achieve better performance.The verification experiments on Weibo and Gossipcop,two widely used real-world datasets,show that FNDB is 4.4%and 0.6%higher in accuracy than the state-of-theart fake news detection methods,respectively.

Divine Simplicity and the Chinese Translation of Theos

Realism is the rational foundation for the translation of the Bible.To translate Theos in Chinese,the distinct terms神,上帝,and天主co-exist and they can be assessed by divine simplicity rationally and spiritually.One of the most difficult problems in Medieval philosophy,divine simplicity is caused by reason serving faith.Reconciling Aristotelian metaphysics with the ultimate object of faith,Aquinas defines that God is being itself and therefore he builds an ontological foundation for Christian theology and perfects divine simplicity.Related concretely with divine simplicity is the simplicity of Theos,so this paper argues that神is the best Chinese translation of Theos to reflect both the truth and the gospel.

Uncover the Aesthetic Simplicity Associated with Mass Transfer in Energy Materials

Aesthetics,referred frequently to as a philosophical term,has played a starring role in forming and evolving a number of aspects of human society,including arts,politics,economics,ethics,etc.Indeed,exploring and investigating the aesthetic phenomena in the scientific field have aroused insightful research findings,which in turn has stimulated research interests in such a science-aesthetics field.In particular,better-evaluated aesthetic aspects of the materials field are expected to be uncovered upon the exceedingly-exposed fundamental breakthroughs in researching the basic structure and functionality of materials.In this report,we glimpse into the aesthetic simplicity of energy materials and comprehend specifically the mass transfer functionalities of key categories of energy materials through an intuitive and bottom-up approach.Our effort aspires to shed new lights on the functionality understanding and manipulation of functional materials in general.

Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content.Because of how clever these videos are frequently,Manipulation is challenging to spot.Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human.In earlier times,it was not so easy to alter the videos,which required expertise in the domain and time.Nowadays,the generation of fake videos has become easier and with a high level of realism in the video.Deepfakes are forgeries and altered visual data that appear in still photos or video footage.Numerous automatic identification systems have been developed to solve this issue,however they are constrained to certain datasets and performpoorly when applied to different datasets.This study aims to develop an ensemble learning model utilizing a convolutional neural network(CNN)to handle deepfakes or Face2Face.We employed ensemble learning,a technique combining many classifiers to achieve higher prediction performance than a single classifier,boosting themodel’s accuracy.The performance of the generated model is evaluated on Face Forensics.This work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges(DFDC)dataset.We test our model using the DFDC,one of the most difficult datasets and get an accuracy of 96%.

Fake News Detection Using Machine Learning and Deep Learning Methods

The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms.Some social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity.To sustain the value of social media,such stories must be distinguished from the true ones.As a result,an automated system is required to save time and money.The classification of fake news and misinformation from social media data corpora is the subject of this research.Several preprocessing and data improvement procedures are used to gather and preprocess two fake news datasets.Deep text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets individually.Bidirectional Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input data.Finally,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news classification.BERT also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL models.In terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

In the past few years,social media and online news platforms have played an essential role in distributing news content rapidly.Consequently.verification of the authenticity of news has become a major challenge.During the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general public.In the first quarter of the year 2020,around 800 people died due to fake news relevant to COVID-19.The major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this manuscript.In addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been contributed.Using the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

Deep Neural Network for Detecting Fake Profiles in Social Networks

This paper proposes a deep neural network(DNN)approach for detecting fake profiles in social networks.The DNN model is trained on a large dataset of real and fake profiles and is designed to learn complex features and patterns that distinguish between the two types of profiles.In addition,the present research aims to determine the minimum set of profile data required for recognizing fake profiles on Facebook and propose the deep convolutional neural network method for fake accounts detection on social networks,which has been developed using 16 features based on content-based and profilebased features.The results demonstrated that the proposed method could detect fake profiles with an accuracy of 99.4%,equivalent to the achieved findings based on bigger data sets and more extensive profile information.The results were obtained with the minimum available profile data.In addition,in comparison with the other methods that use the same amount and kind of data,the proposed deep neural network gives an increase in accuracy of roughly 14%.The proposed model outperforms existing methods,achieving high accuracy and F1 score in identifying fake profiles.The associated findings indicate that the proposed model attained an average accuracy of 99%while considering two distinct scenarios:one with a single theme and another with a miscellaneous one.The results demonstrate the potential of DNNs in addressing the challenging problem of detecting fake profiles,which has significant implications for maintaining the authenticity and trustworthiness of online social networks.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

Deep learning-based approaches are applied successfully in manyfields such as deepFake identification,big data analysis,voice recognition,and image recognition.Deepfake is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artificial intelligence for political abuse,spreading false information,and pornography.The artificial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and ethics.This paper has analyzed the features related to the computer vision of digital content to determine its integrity.This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method.By the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning approach.This proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

Fake News Classification: Past, Current, and Future

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.