Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

Artificial intelligence software for assessing brain ischemic penumbra/core infarction on computed tomography perfusion:A real-world accuracy study

BACKGROUND With the increasingly extensive application of artificial intelligence(AI)in medical systems,the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation.AIM To investigate the accuracy of AI diagnostic software(Shukun)in assessing ischemic penumbra/core infarction in acute ischemic stroke patients due to large vessel occlusion.METHODS From November 2021 to March 2022,consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy(MT)post-Shukun AI penumbra assessment were included.Computed tomography angiography(CTA)and perfusion exams were analyzed by AI,reviewed by senior neurointerventional experts.In the case of divergences among the three experts,discussions were held to reach a final conclusion.When the results of AI were inconsistent with the neurointerventional experts’diagnosis,the diagnosis by AI was considered inaccurate.RESULTS A total of 22 patients were included in the study.The vascular recanalization rate was 90.9%,and 63.6%of patients had modified Rankin scale scores of 0-2 at the 3-month follow-up.The computed tomography(CT)perfusion diagnosis by Shukun(AI)was confirmed to be invalid in 3 patients(inaccuracy rate:13.6%).CONCLUSION AI(Shukun)has limits in assessing ischemic penumbra.Integrating clinical and imaging data(CT,CTA,and even magnetic resonance imaging)is crucial for MT decision-making.

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

Computational intelligence in tropical medicine

The application of computational technology for medical purpose is a very interesting topic.Knowledge content development and new technology search using computational technology becomes the newest approach in medicine.With advanced computational technology,several omics sciences are available for clarification and prediction in medicine.The computational intelligence is an important application that should be mentioned.Here,the author details and discusses on computational intelligence in tropical medicine.

WCCI 2008 CALL FOR PAPERS IEEE World Congress on Computational Intelligence

The 2008 IEEE Wodd Congress on Computational Intelligence （WCCI 2008） will be held at the Hong Kong Convention and Exhibition Centre during June 1-6, 2008. WCCI 2008 will be the fifth milestone in this series with a glorious history from WCCI 1994 in Orlando, WCCI 1998 in Anchorage, WCCI 2002 in Honolulu, to WCCI 2006 in Vancouver. Sponsored by the IEEE Computational Intelligence Society, co-sponsored by the International Neural Network Society, Evolutionary Programming Society, and the Institution of Engineering and Technology, and composed of the 2008 International Joint Conference on Neural Networks （IJCNN2008）, 2008 IEEE International Conference on Fuzzy Syrtems （FUZZ-IEEE2008）, and 2008 IEEE Congress on Evolutionary Computation （CEC2008）, WCC12008 will be the largest technical event on computational intelligence in the world with the biggest impact. WCCI 2008 will provide a stimulating forum for thousands of scientists, engineers, educators and students from all over the world to disseminate their new research findingsand exchange information on emerging areas of research in the fields. WCCI 2008 will also create a pleasant environment for the participants to meet old friends and make new friends who share similar research interests.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

WCCI 2008 CALL FOR PAPERS IEEE World Congress on Computational Intelligence HongKong June 1-6, 2008

)The 2008 IEEE World Congress on Computational Intelligence (WCCI 2008) will be held at the HongKong Convention and Exhibition Centre during June 1-6, 2008. WCCI 2008 will be the fifth milestone inthis series with a glorious history from WCCI 1994 in Orlando, WCCI 1998 in Anchorage, WCCI 2002in Honolulu, to WCCI 2006 in Vancouver. Sponsored by the IEEE Computational Intelligence Society,

Artificial intelligence ecosystem for computational psychiatry:Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms.One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable.This may help researchers develop more effective treatments and interventions for mental health problems.This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry.The artificial intelligence ecosystem for computational psychiatry includes data acquisition,preparation,modeling,application,and evaluation.This approach allows researchers to integrate data from a variety of sources,such as brain imaging,genetics,and behavioral experiments,to obtain a more complete understanding of mental health conditions.Through the process of data preprocessing,training,and testing,the data that are required for model building can be prepared.By using machine learning,neural networks,artificial intelligence,and other methods,researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors.Despite the continuous development and breakthrough of computational psychiatry,it has not yet influenced routine clinical practice and still faces many challenges,such as data availability and quality,biological risks,equity,and data protection.As we move progress in this field,it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

Innovative Scientific Discoveries:The Role of Intelligent Computing in the Fifth Paradigm Shift

This article explores the key role of intelligent computing in driving the paradigm shift of scientific discovery.The article first outlines the five paradigms of scientific discovery,from empirical observation to theoretical models,then to computational simulation and data intensive science,and finally introduces intelligent computing as the core of the fifth paradigm.Intelligent computing enhances the ability to understand,predict,and automate scientific discoveries of complex systems through technologies such as deep learning and machine learning.The article further analyzes the applications of intelligent computing in fields such as bioinformatics,astronomy,climate science,materials science,and medical image analysis,demonstrating its practical utility in solving scientific problems and promoting knowledge development.Finally,the article predicts that intelligent computing will play a more critical role in future scientific research,promoting interdisciplinary integration,open science,and collaboration,providing new solutions for solving complex problems.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Analog Optical Computing for Artificial Intelligence

The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.

Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients:A retrospective study

BACKGROUND:Computed tomography(CT)is a noninvasive imaging approach to assist the early diagnosis of pneumonia.However,coronavirus disease 2019(COVID-19)shares similar imaging features with other types of pneumonia,which makes differential diagnosis problematic.Artificial intelligence(AI)has been proven successful in the medical imaging field,which has helped disease identification.However,whether AI can be used to identify the severity of COVID-19 is still underdetermined.METHODS:Data were extracted from 140 patients with confirmed COVID-19.The severity of COVID-19 patients(severe vs.non-severe)was defined at admission,according to American Thoracic Society(ATS)guidelines for community-acquired pneumonia(CAP).The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co.,Ltd.was used as the analysis tool to analyze chest CT images.RESULTS:A total of 117 diagnosed cases were enrolled,with 40 severe cases and 77 non-severe cases.Severe patients had more dyspnea symptoms on admission(12 vs.3),higher acute physiology and chronic health evaluation(APACHE)II(9 vs.4)and sequential organ failure assessment(SOFA)(3 vs.1)scores,as well as higher CT semiquantitative rating scores(4 vs.1)and AI-CT rating scores than non-severe patients(P<0.001).The AI-CT score was more predictive of the severity of COVID-19(AUC=0.929),and ground-glass opacity(GGO)was more predictive of further intubation and mechanical ventilation(AUC=0.836).Furthermore,the CT semiquantitative score was linearly associated with the AI-CT rating system(Adj R2=75.5%,P<0.001).CONCLUSIONS:AI technology could be used to evaluate disease severity in COVID-19 patients.Although it could not be considered an independent factor,there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.

ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

Computational Intelligence Determines Effective Rationality

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.

Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography

Artificial intelligence(AI),particularly the deep learning technology,have been proven influential to radiology in the recent decade.Its ability in image classification,segmentation,detection and reconstruction tasks have substantially assisted diagnostic radiology,and has even been viewed as having the potential to perform better than radiologists in some tasks.Gastrointestinal radiology,an important subspecialty dealing with complex anatomy and various modalities including endoscopy,have especially attracted the attention of AI researchers and engineers worldwide.Consequently,recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology,particularly in the fields for which public databases are available,such as diagnostic abdominal magnetic resonance imaging(MRI)and computed tomography(CT).This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology,with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and CT.For fields where AI is less developed,this review will also explain the difficulty in AI model training and possible strategies to overcome the technical issues.The authors’insights of possible future development will be addressed in the last section.

Privacy-Enhanced Data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

A significant number of cloud storage environments are already implementing deduplication technology.Due to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is required.As storage capacity increases,additional storage solutions are required.By leveraging deduplication,you can fundamentally solve the cost problem.However,deduplication poses privacy concerns due to the structure itself.In this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve it.In the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is maintained.In addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing attacks.The proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.