Neural Computing & Applications最新文献

{"title":"DSmishSMS-A System to Detect Smishing SMS.","authors":"Sandhya Mishra,&nbsp;Devpriya Soni","doi":"10.1007/s00521-021-06305-y","DOIUrl":"https://doi.org/10.1007/s00521-021-06305-y","url":null,"abstract":"<p><p>With the origin of smart homes, smart cities, and smart everything, smart phones came up as an area of magnificent growth and development. These devices became a part of daily activities of human life. This impact and growth have made these devices more vulnerable to attacks than other devices such as desktops or laptops. Text messages or SMS (Short Text Messages) are a part of smartphones through which attackers target the users. Smishing (SMS Phishing) is an attack targeting smartphone users through the medium of text messages. Though smishing is a type of phishing, it is different from phishing in many aspects like the amount of information available in the SMS, the strategy of attack, etc. Thus, detection of smishing is a challenge in the context of the minimum amount of information shared by the attacker. In the case of smishing, we have short text messages which are often in short forms or in symbolic forms. A single text message contains very few smishing-related features, and it consists of abbreviations and idioms which makes smishing detection more difficult. Detection of smishing is a challenge not only because of features constraint but also due to the scarcity of real smishing datasets. To differentiate spam messages from smishing messages, we are evaluating the legitimacy of the URL (Uniform Resource Locator) in the message. We have extracted the five most efficient features from the text messages to enable the machine learning classification using a limited number of features. In this paper, we have presented a smishing detection model comprising of two phases, Domain Checking Phase and SMS Classification Phase. We have examined the authenticity of the URL in the SMS which is a crucial part of SMS phishing detection. In our system, Domain Checking Phase scrutinizes the authenticity of the URL. SMS Classification Phase examines the text contents of the messages and extracts some efficient features. Finally, the system classifies the messages using Backpropagation Algorithm and compares results with three traditional classifiers. A prototype of the system has been developed and evaluated using SMS datasets. The results of the evaluation achieved an accuracy of 97.93% which shows the proposed method is very efficient for the detection of smishing messages.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 7","pages":"4975-4992"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06305-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10691025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak.","authors":"Deepika Varshney,&nbsp;Dinesh Kumar Vishwakarma","doi":"10.1007/s00521-022-07938-3","DOIUrl":"https://doi.org/10.1007/s00521-022-07938-3","url":null,"abstract":"<p><p>Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5999-6013"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9660173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10801914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety.","authors":"Carmen Vidaurre,&nbsp;Vadim V Nikulin,&nbsp;Maria Herrojo Ruiz","doi":"10.1007/s00521-022-07847-5","DOIUrl":"https://doi.org/10.1007/s00521-022-07847-5","url":null,"abstract":"<p><p>Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5737-5749"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10820887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Communicative capital: a key resource for human-machine shared agency and collaborative capacity.","authors":"Kory W Mathewson,&nbsp;Adam S R Parker,&nbsp;Craig Sherstan,&nbsp;Ann L Edwards,&nbsp;Richard S Sutton,&nbsp;Patrick M Pilarski","doi":"10.1007/s00521-022-07948-1","DOIUrl":"10.1007/s00521-022-07948-1","url":null,"abstract":"<p><p>In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human-machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce <i>communicative capital</i> as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 23","pages":"16805-16819"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10201124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.","authors":"R Rathipriya,&nbsp;Abdul Aziz Abdul Rahman,&nbsp;S Dhamodharavadhani,&nbsp;Abdelrhman Meero,&nbsp;G Yoganandan","doi":"10.1007/s00521-022-07889-9","DOIUrl":"https://doi.org/10.1007/s00521-022-07889-9","url":null,"abstract":"<p><p>Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 2","pages":"1945-1957"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10517070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.","authors":"Hongwei Ding,&nbsp;Leiyang Chen,&nbsp;Qi Tao,&nbsp;Zhongwang Fu,&nbsp;Liang Dong,&nbsp;Xiaohui Cui","doi":"10.1007/s00521-021-06329-4","DOIUrl":"https://doi.org/10.1007/s00521-021-06329-4","url":null,"abstract":"<p><p>The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 7","pages":"5015-5031"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06329-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10275922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent traceability method of water pollution based on dynamic multi-mode optimization.","authors":"Qinghua Wu,&nbsp;Bin Wu,&nbsp;Xuesong Yan","doi":"10.1007/s00521-022-07002-0","DOIUrl":"https://doi.org/10.1007/s00521-022-07002-0","url":null,"abstract":"<p><p>Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source's characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution's real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes' distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions' number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm's historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm's effectiveness in solving problems-accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 3","pages":"2059-2076"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10537119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data.","authors":"Sanghyub John Lee,&nbsp;JongYoon Lim,&nbsp;Leo Paas,&nbsp;Ho Seok Ahn","doi":"10.1007/s00521-023-08276-8","DOIUrl":"https://doi.org/10.1007/s00521-023-08276-8","url":null,"abstract":"<p><p>Tactics to determine the emotions of authors of texts such as Twitter messages often rely on multiple annotators who label relatively small data sets of text passages. An alternative method gathers large text databases that contain the authors' self-reported emotions, to which artificial intelligence, machine learning, and natural language processing tools can be applied. Both approaches have strength and weaknesses. Emotions evaluated by a few human annotators are susceptible to idiosyncratic biases that reflect the characteristics of the annotators. But models based on large, self-reported emotion data sets may overlook subtle, social emotions that human annotators can recognize. In seeking to establish a means to train emotion detection models so that they can achieve good performance in different contexts, the current study proposes a novel transformer transfer learning approach that parallels human development stages: (1) detect emotions reported by the texts' authors and (2) synchronize the model with social emotions identified in annotator-rated emotion data sets. The analysis, based on a large, novel, self-reported emotion data set (<i>n</i> = 3,654,544) and applied to 10 previously published data sets, shows that the transfer learning emotion model achieves relatively strong performance.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 15","pages":"10945-10956"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9721080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State-of-the-art session key generation on priority-based adaptive neural machine (PANM) in telemedicine.","authors":"Joydeep Dey","doi":"10.1007/s00521-022-08169-2","DOIUrl":"https://doi.org/10.1007/s00521-022-08169-2","url":null,"abstract":"<p><p>Telemedicine is one of the safest methods to provide healthcare facilities to the remote patients with the help of digitization. In this paper, state-of-the-art session key has been proposed based on the priority oriented neural machines followed by its validation. State-of-the-art technique can be mentioned as newer scientific method. Soft computing has been extensively used and modified here under the ANN domain. Telemedicine facilitates secure data communication between the patients and the doctors regarding their treatments. The best fitted hidden neuron can contribute only in the formation of the neural output. Minimum correlation was taken into consideration under this study. Hebbian learning rule was applied on both the patient's neural machine and the doctor's neural machine. Lesser iterations were needed in the patient's machine and the doctor's machine for the synchronization. Thus, the key generation time has been shortened here which were 4.011 ms, 4.324 ms, 5.338 ms, 5.691 ms, and 6.105 ms for 56 bits, 128 bits, 256 bits, 512 bits, and 1024 bits of state-of-the-art session keys, respectively. Statistically, different key sizes of the state-of-the-art session keys were tested and accepted. Derived value-based function had yielded successful outcomes too. Partial validations with different mathematical hardness had been imposed here too. Thus, the proposed technique is suitable for the session key generation and authentication in the telemedicine in order to preserve the patients' data privacy. This proposed method has been highly protective against numerous data attacks inside the public networks. Partial transmission of the state-of-the-art session key disables the intruders to decode the same bit patterns of the proposed set of keys.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 13","pages":"9517-9533"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10032630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9752892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a compressed FCN architecture for semantic segmentation using Particle Swarm Optimization.","authors":"Mohit Agarwal,&nbsp;Suneet K Gupta,&nbsp;K K Biswas","doi":"10.1007/s00521-023-08324-3","DOIUrl":"https://doi.org/10.1007/s00521-023-08324-3","url":null,"abstract":"<p><p>Researchers have adapted the conventional deep learning classification networks to generate Fully Conventional Networks (FCN) for carrying out accurate semantic segmentation. However, such models are expensive both in terms of storage and inference time and not readily employable on edge devices. In this paper, a compressed version of VGG16-based Fully Convolution Network (FCN) has been developed using Particle Swarm Optimization. It has been shown that the developed model can offer tremendous saving in storage space and also faster inference time, and can be implemented on edge devices. The efficacy of the proposed approach has been tested using potato late blight leaf images from publicly available PlantVillage dataset, street scene image dataset and lungs X-Ray dataset and it has been shown that it approaches the accuracies offered by standard FCN even after 851× compression.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 16","pages":"11833-11846"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9855405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}