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The protection of digital content is increasingly becoming a significant issue for researchers and engineers. In this context, nonlinear dynamic systems play a vital role in information security through their chaotic behavior and susceptibility to initial conditions. This research presents a 3D chaotic map-based symmetric algorithm for multiple images to improve encryption efficiency and encourage secure transmission. The proposed scheme comprises the following four modules: the combination (the images are combined into a single image by merging their color channels); the permutation (using the suggested 3D chaotic map); the S-box generation; and the substitution through the AES substitution method. The proposed algorithm’s encryption strength was determined through Entropy, Correlation coefficient, NPCR, and UACI analyses, which were then compared to the past techniques. Furthermore, the proposed method is assessed in terms of its computation time. Results demonstrate that it is highly efficient and secure for real-time communication.

Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readily by traditional diagnostic techniques. This research employed deep learning long short-term memory $(LSTM)$ and Residual Net $(ResNet-101)$ , independent of hand-crafted features, and is fine-tuned. The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix $(GLCM)$ using non-deep learning classifiers such as support vector machine $(SVM)$ Gaussian Kernel, k-nearest neighbor-Cosine $(KNN-Cosine)$ , kernel naive Bayes, decision tree $(DT)$ and RUSBoost tree. This study reduces the features of carcinoma images, employed machine learning and deep learning approaches. For validation of training and testing data, a jack-knife ten-fold cross-validation method was used. The performance was measured using a confusion matrix such as sensitivity, specificity, positive predictive value $(PPV)$ , negative predictive value $(NPV)$ , accuracy $(AC)$ , Mathews Correlation Coefficient ( $MCC$ ), and area under the curve $(AUC)$ . The most remarkable performance was obtained using non-deep learning methods with $GLCM$ features using KNN-Cosine with sensitivity (98.00%), specificity (99.25%), PPV (98.99%), NPV (99.11%), accuracy (99.07%), and AUC (0.998). The LSTM deep learning method yields performance with sensitivity (98.33%), specificity (100%), PPV (100%), NPV (99.26%), accuracy (99.48%), MCC (0.9879) and AUC (0.9999), where using Deep learning method $ResNet-101$ , we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that $ResNet-101$ deep learning outperformed than non-deep learning methods and $LSTM$ . Thus, the deep learning method $ResNet-101$ could be used as a better predictor for the detection of prostate cancer.

Massive advances in internet infrastructure are impacting e-healthcare services compared to conventional means. Therefore, extra care and protection is needed for extremely confidential patient medical records. With this intention, we have proposed an enhanced image steganography method, to improve imperceptibility and data hiding capacity of stego images. The proposed Image Region Decomposition (IRD) method, embeds more secret information with better imperceptibility, in patient’s medical images. The algorithm decomposes the grayscale magnetic resonance imaging (MRI) images into three unique regions: low-intensity, medium-intensity, and high-intensity. Each region is made up of k number of pixels, and in each pixel we operate the block of n least significant bits (LSBs), where 1 ≤ n ≤ 3. Four classes of MRI images of different dimensions are used for embedding. Data with different volumes are used to test the images for imperceptibility and verified with quality factors. The proposed IRD algorithm is tested for performance, on the set of brain MRI images using peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity (SSIM) index. The results elucidated that the MRI stego image is imperceptible, like the original cover image by adjusting 2nd and 1st LSBs in the low-intensity region. Our proposed steganography technique provides a better average PSNR (49.27), than other similar methods. The empirical results show that the proposed IRD algorithm, significantly improves the imperceptibility and data embedding capacity, compared to the existing state-of-the-art methods. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Electronic devices are integral part of our life and modeling their lifetime is the most challenging and interesting field in reliability analysis. To investigate the failure behavior of electronic devices reliability analysis is commonly used. In the literature, however, it is reported that one in five electronic device failure is a result of corrosion and to save electricity and predict future failures, it is important to summarize the data by some flexible probability models. This will not only help the electronic companies, but also the users by providing them information about the maximum voltage level that a particular device can bear. This article deals with the reliability analysis of electronic devices under different voltages assuming modified generalized exponential distribution and beta generalized exponential distribution using the inverse power law rule. The parameters of the modified distribution are estimated assuming Bayesian inference to include prior information. Sensitivity of hyperparameters and selection of an appropriate probability model is also a part of this study.

Artificially intelligent (AI) game software incorporates different algorithms to generate intelligent human-like responses to the users playing them. Testing AI game software poses great difficulty because of the complex possibilities that can result at a given point and analysis of said possibilities is a tedious task. Also during software development there are resource constraints due to which testing targets specific parts of the software. An AI game of Chess takes into consideration a large amount of possible outcomes at any given point before deciding a move. Therefore, testing it in its entirety is impractical. In this paper we propose a metamorphic testing approach for testing an AI Chess game i.e. a Chess engine’s algorithm of determining and pruning out possible outcomes and ultimately deciding on a final outcome. For validating our approach, we have done error seeding on an open source Chess engine and tested it through our approach. The results for our proposed approach for testing an AI Chess game through metamorphic relations show that it is successful in revealing 71% of the total seeded faults. On comparison of our proposed approach with the existing technique of testing Chess engine i.e., perft function, we have come across situations in which our proposed approach reveals errors overlooked by the existing technique. In the future we aim to extend our approach towards other AI game software.

The wireless networks face challenges in efficient utilization of bandwidth due to paucity of resources and lack of central management, which may result in undesired congestion. The cognitive radio (CR) paradigm can bring efficiency, better utilization of bandwidth, and appropriate management of limited resources. While the CR paradigm is an attractive choice, the CRs selfishly compete to acquire and utilize available bandwidth that may ultimately result in inappropriate power levels, causing degradation in network’s Quality of Service (QoS). A cooperative game theoretic approach can ease the problem of spectrum sharing and power utilization in a hostile and selfish environment. We focus on the challenge of congestion control that results in inadequate and uncontrolled access of channels and utilization of resources. The Nash equilibrium (NE) of a cooperative congestion game is examined by considering the cost basis, which is embedded in the utility function. The proposed algorithm inhibits the utility, which leads to the decrease in aggregate cost and global function maximization. The cost dominance is a pivotal agent for cooperation in CRs that results in efficient power allocation. Simulation results show reduction in power utilization due to improved management in cognitive radio resource allocation.

Social media is a vital source to produce textual data, further utilized in various research fields. It has been considered an essential foundation for organizations to get valuable data to assess the users’ thoughts and opinions on a specific topic. Text classification is a procedure to assign tags to predefined classes automatically based on their contents. The aspect-based sentiment analysis to classify the text is challenging. Every work related to sentiment analysis approached this issue as the current research usually discusses the document-level and overall sentence-level analysis rather than the particularities of the sentiments. This research aims to use Twitter data to perform a finer-grained sentiment analysis at aspect-level by considering explicit and implicit aspects. This study proposes a new Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach by embedding a feature ranking process with an amendment of feature selection method for Twitter and sentiment classification comprising of Artificial Neural Network; Multi-Layer Perceptron (MLP) is used to attain improved results. In this study, different machine learning classification methods were also implemented, including Random Forest (RF), Support Vector Classifier (SVC), and seven more classifiers to compare with the proposed classification method. The implementation of the proposed hybrid method has shown better performance and the efficiency of the proposed system was validated on multiple Twitter datasets to manifest different domains. We achieved better results for all Twitter datasets used for the validation purpose of the proposed method with an accuracy of 78.99%, 84.09%, 80.38%, 82.37%, and 84.72%, respectively, compared to the baseline approaches. The proposed approach revealed that the new hybrid aspect-based text classification functionality is enhanced, and it outperformed the existing baseline methods for sentiment classification.

A pneumonia-like disease of unknown origin caused a catastrophe in Wuhan city, China. This disease spread to 215 countries affecting a wide range of people. World health organization (WHO) called it a pandemic and it was officially named as Severe Acute Respiratory Syndrome Corona virus 2 (SARS CoV-2), also known as Corona virus disease (COVID-19). This pandemic compelled countries to enforce a socio-economic lockdown to prevent its widespread. This paper focuses on how the particulate matter pollution was reduced during the lockdown period (23 March to April 15, 2020) as compared to before lockdown. Both ground-based and satellite observations were used to identify the improvement in air quality of Pakistan with primary focus on four major cities of Lahore, Islamabad, Karachi and Peshawar. Both datasets have shown a substantial reduction in PM2.5 pollution levels (ranging from 13% to 33% in case of satellite observations, while 23%–58% in ground-based observations) across Pakistan. Result shows a higher rate of COVID-19 spread in major cities of Pakistan with poor air quality conditions. Yet more research is needed in order to establish linkage between COVID-19 spread and air pollution. However, it can be partially attributed to both higher rate of population density and frequent exposure of population to enhanced levels of PM2.5 concentrations before lockdown period., Highlights • The COVID-19 has caused severe health concerns and economic disruptions worldwide. • Larger spread of COVID-19 can be associated with elevated levels of PM2.5 • The lockdown had a positive change in severely polluted cities of Pakistan. • Besides improvement, it has posed sever challenges of environmental monitoring. • Restricted mobility resulted in pile up and improper disposal of hospital waste.

With dynamic changes in world’s technology, an increasing growth and adoption observed in the usage of social media, computer networks, internet of things, and cloud computing. Research experiments are also generating huge amount of data which are to be collected, managed and analyzed. This huge data is known as "Big Data". Research analysts have perceived an increase in data that contains both useful and useless entities. In extraction of useful information, data warehouse finds difficulties in enduring with increasing amount of data generated. With shifts in paradigm, big data analytics emerged as promising area of research which supports business intelligence in terms of decision making. This paper provides a comprehensive survey on BigData, BigData problems, BigData Analytics and Big Data Warehouse. In addition, it also explains how the need for augmentation of big data and data warehouse emerged in perspective of decision making, comparing methods and research problems. It also elaborates applications which support Big Data, Data Warehouse, and its challenges.

Trust modeling has received a remarkable attention in recent research. It is a decision making procedure which quantifies the major concern in information exchange, social exchange, and cognitive motivation. Trust has temporal behavior and it grows and declines on the basis of some cogitative parameters. Current study is based on psychological approach through which complicated relationships among different individuals can be model. In this study, we have proposed a computation cognitive trust model between pet and owner using difference equations. Results revels that proposed model have similar response as the assumptions was made. Moreover results were also obtained using simulation language.

A pneumonia-like disease of unknown origin caused a catastrophe in Wuhan city, China. This disease spread to 215 countries affecting a wide range of people. World health organization (WHO) called it a pandemic and it was officially named as Severe Acute Respiratory Syndrome Corona virus 2 (SARS CoV-2), also known as Corona virus disease (COVID-19). This pandemic compelled countries to enforce a socio-economic lockdown to prevent its widespread. This paper focuses on how the particulate matter pollution was reduced during the lockdown period (23 March to April 15, 2020) as compared to before lockdown. Both ground-based and satellite observations were used to identify the improvement in air quality of Pakistan with primary focus on four major cities of Lahore, Islamabad, Karachi and Peshawar. Both datasets have shown a substantial reduction in PM2.5 pollution levels (ranging from 13% to 33% in case of satellite observations, while 23%-58% in ground-based observations) across Pakistan. Result shows a higher rate of COVID-19 spread in major cities of Pakistan with poor air quality conditions. Yet more research is needed in order to establish linkage between COVID-19 spread and air pollution. However, it can be partially attributed to both higher rate of population density and frequent exposure of population to enhanced levels of PM2.5 concentrations before lockdown period.

Temperature is a key indicator of a changing climate. A trivial change in temperature can result in significant changes in weather patterns with severe repercussions on ecology and socio-economic conditions of an area. This study aims to assess the changing trends in maximum and minimum temperatures in five climatic zones of Pakistan, and compare ground-based with satellite data from AIRS over these climatic zones of Pakistan. Mann Kendall test has been used to signify the temperature trends in all zones and seasons while Sen's slope value has been used to measure the magnitude of the trends for the time period 1978 to 2016. Ground observations have been compared with AIRS satellite observations for ascending and descending air temperatures by employing correlation and mean bias error. The results show that AIRS product (AIRSX3STM) underestimates Tmax and Tmin in all zones of Pakistan with the underestimation being most prominent in zone A. The trend analysis for maximum and minimum temperatures shows an increase in temperatures for all climate zones. Overall, Pakistan's maximum temperature increased by 0.026°C/year with the maximum increase of 0.05°C/year during pre-monsoon, and the minimum temperature increased by 0.027°C/year.

In aiming for behavioral fidelity, artificial intelligence cannot and no longer ignores the formalization of human affect. Affect modeling plays a vital role in faithfully simulating human emotion and in emotionally-evocative technology that aims at being real. This paper offers a short expose about three models concerning the regulation and generation of affect: CoMERG, EMA and I-PEFiC^A^D^M, which each in their own right are successfully applied in the agent and robot domain. We argue that the three models partly overlap and where distinct, they complement one another. To enable their integration, we provide an analysis of the theoretical concepts, resulting in a more precise representation of affect simulation in virtual humans, which we verify with simulation tests.

In order to enhance user involvement in financial services, this paper proposes to combine the idea of adaptive personalisation with intelligent virtual agents. To this end, a computational model for human decision making in financial context is incorporated within an intelligent virtual agent. To test whether the agent enhances user involvement, a web application has been developed, in which users have to make a number of investment decisions. This application has been evaluated in an experiment for a number of participants interacting with the system and afterwards providing their judgement by means of a questionnaire. The preliminary results indicate that the virtual agent can show appropriate emotional expressions related to states like happiness, greed and fear, and has high potential to enhance user involvement.

In the area of financial decision making it is more and more acknowledged that psychological states and characteristics play an important role, for example feeling insecure in relation to financial risks, and being greedy in relation to opportunities to obtain serious gains. This paper presents an agent model of human decision making behaviour in economic situations, incorporating a human’s greed state and personality characteristic concerning risk. The model provides a basis for the development of personalised intelligent agents that support a person in financial decisions. To evaluate the model a number of simulation experiments have been performed, which illustrate the model’s ability to show behaviour of different types of personalities.

In aiming for behavioral fidelity, artificial intelligence cannot and no longer ignores the formalization of human affect. Affect modeling plays a vital role in faithfully simulating human emotion and in emotionally-evocative technology that aims at being real. This paper offers a short expose about three models concerning the generation and regulation of affect: CoMERG, EMA and I-PEFiCADM, which each in their own right are successfully applied in the agent and robot domain. We argue that the three models partly overlap and where distinct, they complement one another. We provide an analysis of the theoretical concepts, and provide a blueprint of an integration, which should result in a more precise representation of affect simulation in virtual humans.

A classical debate in economics addresses the advantages and drawbacks of modelling from a macroeconomics perspective as opposed to modelling from a microeconomics perspective. Form the latter psychological aspects at an individual level can be taken into account in a differentiated manner. Within computer science and AI, a similar debate exists about the differences between agent-based and population-based modelling. This paper aligns both debates by exploring the differences and commonalities between populationbased and agent-based modelling in economical context. A case study is performed on the interplay between individual greed as a psychological concept and global economical concepts. It is shown that under certain conditions agentbased and population-based simulations show similar results.

In earlier studies, user involvement with an embodied software agent and willingness to use that agent were partially determined by the aesthetics of the design and the moral fiber of the character. We used these empirical results to model agents that in their turn would build up affect for their users much the same way as humans do for agents. Through simulations, we tested these models for internal consistency and were successful in establishing the relationships among the factors as suggested by the earlier user studies. This paper reports on the first confrontation of our agent system with real users to check whether users recognize that our agents function in similar ways as humans do. Through a structured questionnaire, users informed us whether our agents evaluated the user's aesthetics and moral stance while building up a level of involvement with the user and a degree of willingness to interact with the user again.

Affect modeling plays a vital role in faithfully simulating human emotion and in emotionally evocative technology. Current affect models are still strong simplifications compared to human affective complexity. To establish richer agent interaction, we integrated three affect-related models: CoMERG, IPEFiC ADM and EMA. These models partly overlap, and where distinct, they complement one another. The integrated model called Silicon Coppelia was implemented and simulation experiments were performed to test the behavior of the model. These experiments show that the model can simulate richer agent behaviors than any of the models could have done alone.

To create a robot with a mind of its own, we extended a formalized version of a model that explains affect-driven interaction with mechanisms for goal-directed behavior. We ran simulation experiments with intelligent software agents and found that agents preferred affect-driven decision options to rational decision options in situations where choices for low expected utility are irrational. This behavior counters current models in decision making, which generally have a hedonic bias and always select the option with the highest expected utility.

This paper presents an approach to incorporate emotion regulation as addressed within psychology literature into virtual characters. To this end, first Gross' informal theory of emotion regulation has been formalised using a dynamical system style modelling approach. Next, a virtual environment has been created, involving a number of virtual agents, which have been equipped with the formalised model for emotion regulation. This environment has been used to successfully generate a number of emergent virtual stories, in which characters regulate their emotions by applying regulation strategies such as situation selection and attentional deployment. The behaviours shown in the stories were found consistent with descriptions of human regulation processes.