Pub Date : 2021-08-13DOI: 10.1080/0952813X.2021.1964003
S. Mclean, G. Read, Jason Thompson, Chris Baber, N. Stanton, P. Salmon
ABSTRACT Artificial General intelligence (AGI) offers enormous benefits for humanity, yet it also poses great risk. The aim of this systematic review was to summarise the peer reviewed literature on the risks associated with AGI. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen articles were deemed eligible for inclusion. Article types included in the review were classified as philosophical discussions, applications of modelling techniques, and assessment of current frameworks and processes in relation to AGI. The review identified a range of risks associated with AGI, including AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks. Several limitations of the AGI literature base were also identified, including a limited number of peer reviewed articles and modelling techniques focused on AGI risk, a lack of specific risk research in which domains that AGI may be implemented, a lack of specific definitions of the AGI functionality, and a lack of standardised AGI terminology. Recommendations to address the identified issues with AGI risk research are required to guide AGI design, implementation, and management.
{"title":"The risks associated with Artificial General Intelligence: A systematic review","authors":"S. Mclean, G. Read, Jason Thompson, Chris Baber, N. Stanton, P. Salmon","doi":"10.1080/0952813X.2021.1964003","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1964003","url":null,"abstract":"ABSTRACT Artificial General intelligence (AGI) offers enormous benefits for humanity, yet it also poses great risk. The aim of this systematic review was to summarise the peer reviewed literature on the risks associated with AGI. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen articles were deemed eligible for inclusion. Article types included in the review were classified as philosophical discussions, applications of modelling techniques, and assessment of current frameworks and processes in relation to AGI. The review identified a range of risks associated with AGI, including AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks. Several limitations of the AGI literature base were also identified, including a limited number of peer reviewed articles and modelling techniques focused on AGI risk, a lack of specific risk research in which domains that AGI may be implemented, a lack of specific definitions of the AGI functionality, and a lack of standardised AGI terminology. Recommendations to address the identified issues with AGI risk research are required to guide AGI design, implementation, and management.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"25 1","pages":"649 - 663"},"PeriodicalIF":2.2,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79374218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-11DOI: 10.1080/0952813X.2021.1964614
Dejiao Niu, Le Yang, Tianquan Liu, Tao Cai, Shijie Zhou, Lei Li
ABSTRACT Hierarchical temporal memory is an emerging machine learning technology that aims to model the structural and algorithmic properties of the neocortex. It is particularly suitable for learning and predicting sequential data. However, when dealing with long time series or complex sequences, the accuracy is relatively lower than desired. In this paper, a novel hierarchical temporal memory based on recurrent learning unit is proposed, where a feedback mechanism is involved into the model. The original cell is extended with a recurrent unit to capture long temporal dependencies of synaptic connections between neurons. The temporal pooler algorithm is then modified to adapt to the recurrent learning unit, and the supervised gradient information is combined with the Hebbian synaptogenesis learning rule in speeding up the training. The prototype of the proposed hierarchical temporal memory is implemented and extensive experiments are carried out on two public datasets under various settings. Experimental results show that the proposed model obtains an accuracy increase by up to 32% and a perplexity drop by up to 14% on sequence prediction and text generation tasks, respectively, which indicates the hierarchical temporal memory with recurrent feedback outperforms the original model on sequence learning.
{"title":"A new hierarchical temporal memory based on recurrent learning unit","authors":"Dejiao Niu, Le Yang, Tianquan Liu, Tao Cai, Shijie Zhou, Lei Li","doi":"10.1080/0952813X.2021.1964614","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1964614","url":null,"abstract":"ABSTRACT Hierarchical temporal memory is an emerging machine learning technology that aims to model the structural and algorithmic properties of the neocortex. It is particularly suitable for learning and predicting sequential data. However, when dealing with long time series or complex sequences, the accuracy is relatively lower than desired. In this paper, a novel hierarchical temporal memory based on recurrent learning unit is proposed, where a feedback mechanism is involved into the model. The original cell is extended with a recurrent unit to capture long temporal dependencies of synaptic connections between neurons. The temporal pooler algorithm is then modified to adapt to the recurrent learning unit, and the supervised gradient information is combined with the Hebbian synaptogenesis learning rule in speeding up the training. The prototype of the proposed hierarchical temporal memory is implemented and extensive experiments are carried out on two public datasets under various settings. Experimental results show that the proposed model obtains an accuracy increase by up to 32% and a perplexity drop by up to 14% on sequence prediction and text generation tasks, respectively, which indicates the hierarchical temporal memory with recurrent feedback outperforms the original model on sequence learning.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"3 1","pages":"665 - 678"},"PeriodicalIF":2.2,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84724648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-08DOI: 10.1080/0952813X.2021.1958063
Priyanka Dwivedi, A. K. Sarkar, Chinmay Chakraborty, M. Singha, Vineet Rojwal
ABSTRACT Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions.
{"title":"Application of Artificial Intelligence on Post Pandemic Situation and Lesson Learn for Future Prospects","authors":"Priyanka Dwivedi, A. K. Sarkar, Chinmay Chakraborty, M. Singha, Vineet Rojwal","doi":"10.1080/0952813X.2021.1958063","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1958063","url":null,"abstract":"ABSTRACT Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"1 1","pages":"327 - 344"},"PeriodicalIF":2.2,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83126481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-03DOI: 10.1080/0952813X.2021.1955980
Kanwal Nisar, Muhammad Shaheen
ABSTRACT Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods.
{"title":"Determining context of association rules by using machine learning","authors":"Kanwal Nisar, Muhammad Shaheen","doi":"10.1080/0952813X.2021.1955980","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1955980","url":null,"abstract":"ABSTRACT Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"6 3 Pt 1 1","pages":"59 - 76"},"PeriodicalIF":2.2,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83277802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-03DOI: 10.1080/0952813X.2021.1957024
S. Szczȩsny, Damian Huderek, Lukasz Przyborowski
ABSTRACT The work presents a concept of an implementation of an explainable artificial intelligence (XAI) using effective models of third-generation neurons. The article discusses a concept of building a neural network based on spiking neurons modelled on ladder nervous systems. A distinction is made between voltage signals encoding information in a network and current signals which contain the correlation between information in the network and pattern features. Analyzes feature a neuron model based on the cusp catastrophe theory eliminating network sensitivity to problems of synapse plasticity, weight mismatch and coupling of neurons based on electric models. The paper presents applications of a spiking neural network for reporting the state of water quality while generating justifications. The article contains results of an analysis of confusion of justifications with ACC = 1 for a set of 10,000 patterns. It also discusses the speed of pattern analysis in the simulated network.
{"title":"Explainable spiking neural network for real time feature classification","authors":"S. Szczȩsny, Damian Huderek, Lukasz Przyborowski","doi":"10.1080/0952813X.2021.1957024","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1957024","url":null,"abstract":"ABSTRACT The work presents a concept of an implementation of an explainable artificial intelligence (XAI) using effective models of third-generation neurons. The article discusses a concept of building a neural network based on spiking neurons modelled on ladder nervous systems. A distinction is made between voltage signals encoding information in a network and current signals which contain the correlation between information in the network and pattern features. Analyzes feature a neuron model based on the cusp catastrophe theory eliminating network sensitivity to problems of synapse plasticity, weight mismatch and coupling of neurons based on electric models. The paper presents applications of a spiking neural network for reporting the state of water quality while generating justifications. The article contains results of an analysis of confusion of justifications with ACC = 1 for a set of 10,000 patterns. It also discusses the speed of pattern analysis in the simulated network.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"1 1","pages":"77 - 92"},"PeriodicalIF":2.2,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83900477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Reinforcement learning holds considerable promise to help address sequential decision-making problems, in which Q-learning is one of the most used algorithms. However, Q-learning suffers from overestimation errors, especially when action values in the same state are similar. To reduce such damages, we introduce an internal supplemental action measurement based on the variation of the expected state values between a state transition, which can measure the action causing the state transition. For the reason that the internal supplemental action measurement can increase or decrease the action values according to the action performance, it can increase the gap of the action values, thus reducing the sensitivity to the overestimation error. The experimental results in the Markov chain and the video games demonstrate the performance advantage of applying the internal supplemental action measurement, in which the mean evaluating scores with the internal supplemental action measurement are 131.6% in SpaceInvaders, 187.9% in Seaquest, and 176.6% in Asterix respectively of that without the internal supplemental action measurement.
{"title":"An internal supplemental action measurement to increase the gap of action values and reduce the sensitivity to overestimation error","authors":"Haolin Wu, Hui Li, Jianwei Zhang, Zhuang Wang, Zhiyong Huang","doi":"10.1080/0952813X.2021.1955017","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1955017","url":null,"abstract":"ABSTRACT Reinforcement learning holds considerable promise to help address sequential decision-making problems, in which Q-learning is one of the most used algorithms. However, Q-learning suffers from overestimation errors, especially when action values in the same state are similar. To reduce such damages, we introduce an internal supplemental action measurement based on the variation of the expected state values between a state transition, which can measure the action causing the state transition. For the reason that the internal supplemental action measurement can increase or decrease the action values according to the action performance, it can increase the gap of the action values, thus reducing the sensitivity to the overestimation error. The experimental results in the Markov chain and the video games demonstrate the performance advantage of applying the internal supplemental action measurement, in which the mean evaluating scores with the internal supplemental action measurement are 131.6% in SpaceInvaders, 187.9% in Seaquest, and 176.6% in Asterix respectively of that without the internal supplemental action measurement.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"49 1","pages":"1047 - 1061"},"PeriodicalIF":2.2,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88473615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.1080/0952813X.2021.1952653
Santiago Núñez Corrales, E. Jakobsson
ABSTRACT Artificial superintelligent (ASI) agents that will not cause harm to humans or other organisms are central to mitigating a growing contemporary global safety concern as artificial intelligent agents become more sophisticated. We argue that it is not necessary to resort to implementing an explicit theory of ethics, and that doing so may entail intractable difficulties and unacceptable risks. We attempt to provide some insight into the matter by defining a minimal set of boundary conditions potentially capable of decreasing the probability of conflict with synthetic intellects intended to prevent aggression towards organisms. Our argument departs from causal entropic forces as good general predictors of future action in ASI agents. We reason that maximising future freedom of action implies reducing the amount of repeated computation needed to find good solutions to a large number of problems, for which living systems are good exemplars: a safe ASI should find living organisms intrinsically valuable. We describe empirically-bounded ASI agents whose actions are constrained by the character of physical laws and their own evolutionary history as emerging from H. sapiens, conceptually and memetically, if not genetically. Plausible consequences and practical concerns for experimentation are characterised, and implications for life in the universe are discussed.
{"title":"Entropic boundary conditions towards safe artificial superintelligence","authors":"Santiago Núñez Corrales, E. Jakobsson","doi":"10.1080/0952813X.2021.1952653","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1952653","url":null,"abstract":"ABSTRACT Artificial superintelligent (ASI) agents that will not cause harm to humans or other organisms are central to mitigating a growing contemporary global safety concern as artificial intelligent agents become more sophisticated. We argue that it is not necessary to resort to implementing an explicit theory of ethics, and that doing so may entail intractable difficulties and unacceptable risks. We attempt to provide some insight into the matter by defining a minimal set of boundary conditions potentially capable of decreasing the probability of conflict with synthetic intellects intended to prevent aggression towards organisms. Our argument departs from causal entropic forces as good general predictors of future action in ASI agents. We reason that maximising future freedom of action implies reducing the amount of repeated computation needed to find good solutions to a large number of problems, for which living systems are good exemplars: a safe ASI should find living organisms intrinsically valuable. We describe empirically-bounded ASI agents whose actions are constrained by the character of physical laws and their own evolutionary history as emerging from H. sapiens, conceptually and memetically, if not genetically. Plausible consequences and practical concerns for experimentation are characterised, and implications for life in the universe are discussed.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"65 1","pages":"1 - 33"},"PeriodicalIF":2.2,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85131117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.1080/0952813X.2021.1952652
Abdullahi Umar Ibrahim, P. C. Pwavodi, M. Ozsoz, F. Al-turjman, T. Galaya, J. J. Agbo
ABSTRACT Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses.
{"title":"Crispr biosensing and Ai driven tools for detection and prediction of Covid-19","authors":"Abdullahi Umar Ibrahim, P. C. Pwavodi, M. Ozsoz, F. Al-turjman, T. Galaya, J. J. Agbo","doi":"10.1080/0952813X.2021.1952652","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1952652","url":null,"abstract":"ABSTRACT Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"41 1","pages":"489 - 505"},"PeriodicalIF":2.2,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78718050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-15DOI: 10.1080/0952813X.2021.1938696
Shabnam Ezatzadeh, M. Keyvanpour, S. V. Shojaedini
ABSTRACT A sudden fall accident is the main concern for the elderly and disabled people. Automatic detection of the falls from video sequences is an assistive technology for surveillance systems. In this study, a three-stage framework was presented and implemented based on the combination of the data from multiple cameras to address the challenges of occlusion and visibility. In the first stage, the number of used cameras was specified. In the second stage, each camera was decided locally based on its data about the fall incident. In the third and final stage, the aggregation function was used to combine the single camera’s decision considering the coverage rate coefficient of the used cameras. Experiments on the multiple-camera fall dataset demonstrated that our method is comparable to other state-of-the-art methods.
{"title":"A human fall detection framework based on multi-camera fusion","authors":"Shabnam Ezatzadeh, M. Keyvanpour, S. V. Shojaedini","doi":"10.1080/0952813X.2021.1938696","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1938696","url":null,"abstract":"ABSTRACT A sudden fall accident is the main concern for the elderly and disabled people. Automatic detection of the falls from video sequences is an assistive technology for surveillance systems. In this study, a three-stage framework was presented and implemented based on the combination of the data from multiple cameras to address the challenges of occlusion and visibility. In the first stage, the number of used cameras was specified. In the second stage, each camera was decided locally based on its data about the fall incident. In the third and final stage, the aggregation function was used to combine the single camera’s decision considering the coverage rate coefficient of the used cameras. Experiments on the multiple-camera fall dataset demonstrated that our method is comparable to other state-of-the-art methods.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"34 1","pages":"905 - 924"},"PeriodicalIF":2.2,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82938780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-15DOI: 10.1080/0952813X.2021.1949755
V. R., Abhishek Kumar, Ankit Kumar, V. A. Ashok Kumar, Rajeshkumar K, V. D. A. Kumar, Abdul Khader Jilani Saudagar, A. A
ABSTRACT The humankind had faced several pandemic outbreaks, and coronavirus illness (COVID-19) caused by severe, acute respiratory syndrome coronavirus 2, is designated an emergency by the World Health Organization (WHO). Recognition of COVID-19 is a challenging task. The most commonly used methods are X-ray and CT scans images to inspect COVID-19 patients. It requires specialised medical professionals to report each patient’s health manually. It is found that COVID-19 shows considerable similarity to pneumonia lung disease. Thus, knowledge learned from a model to diagnose pneumonia can be translated to identify COVID-19. Transfer learning method offers a drastic performance when compared with results from conventional classification. In this study, Image pre-processing is done to alleviate intensity variations between medical images. These processed images undergo a feature extraction which is accomplished using Q-deformed entropy and deep learning extraction. The feature extraction techniques are employed to remove abnormal markers from images, noise impedance from tissues and lesions. The traits acquired are integrated to differentiate between COVID-19, pneumonia and healthy cases. The primary aim of this model is to produce an image processing tool for medical professionals. The model results to inspect how a healthy or COVID-19 individual outperforms conventional models. The maximum accuracy of the collected data set is 99.68%.
{"title":"COVIDPRO-NET: a prognostic tool to detect COVID 19 patients from lung X-ray and CT images using transfer learning and Q-deformed entropy","authors":"V. R., Abhishek Kumar, Ankit Kumar, V. A. Ashok Kumar, Rajeshkumar K, V. D. A. Kumar, Abdul Khader Jilani Saudagar, A. A","doi":"10.1080/0952813X.2021.1949755","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1949755","url":null,"abstract":"ABSTRACT The humankind had faced several pandemic outbreaks, and coronavirus illness (COVID-19) caused by severe, acute respiratory syndrome coronavirus 2, is designated an emergency by the World Health Organization (WHO). Recognition of COVID-19 is a challenging task. The most commonly used methods are X-ray and CT scans images to inspect COVID-19 patients. It requires specialised medical professionals to report each patient’s health manually. It is found that COVID-19 shows considerable similarity to pneumonia lung disease. Thus, knowledge learned from a model to diagnose pneumonia can be translated to identify COVID-19. Transfer learning method offers a drastic performance when compared with results from conventional classification. In this study, Image pre-processing is done to alleviate intensity variations between medical images. These processed images undergo a feature extraction which is accomplished using Q-deformed entropy and deep learning extraction. The feature extraction techniques are employed to remove abnormal markers from images, noise impedance from tissues and lesions. The traits acquired are integrated to differentiate between COVID-19, pneumonia and healthy cases. The primary aim of this model is to produce an image processing tool for medical professionals. The model results to inspect how a healthy or COVID-19 individual outperforms conventional models. The maximum accuracy of the collected data set is 99.68%.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"51 1","pages":"473 - 488"},"PeriodicalIF":2.2,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75585500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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