Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Multiple Perspective of Multipredictor Mechanism and Multihistogram Modification for High-Fidelity Reversible Data Hiding

Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.

Benefits and drawbacks of radiofrequency ablation via percutaneous or minimally invasive surgery for treating hepatocellular carcinoma

The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant impediment.Consequently,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent biases.Ultrasound is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC recurrence.To enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor ablation.Minimally invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical fields.Recent studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its significance.Therefore,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA approach.This article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.

Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

Prototype for Integrating Internet of Things and Emergency Service in an IP Multimedia Subsystem for Wireless Body Area Networks

In recent years, the application of the Internet of Things （IoT） has become an emerging business. The most important concept of next-generation network for providing a common global IT platform is combining seamless networks and networked things, objects or sensors. Also, wireless body area networks （WBANs） are becoming mature with the widespread usage of the IoT. In order to support WBAN, the platform, scenario and emergency service are necessary due to the sensors in WBAN being related to wearer＆#39;s life. The sensors on the body detect a lot of information about bioinformatics and medical signals, such as heartbeat and blood. Thus, the integration of IoT and network communication in daily life is important. However, there is not only a lack of common fabric for integrating IoT with current Internet and but also no emergency call process in the current network communication envi-ronment. To overcome such situations, the prototype of integrating IoT and emergency call process is discussed. A simulated boot-strap platform to provide the discussion of open challenges and solutions for deploying IoT in Internet and the emergency commu-nication system are analyzed by using a service of 3GPP IP multimedia subsystem. Finally, the prototype for supporting WBAN with emergence service is also addressed and the performance results are useful to service providers and network operators that they can estimate their migration to IoT by referring to this experience and experiment results. Furthermore, the queuing model used to achieve the performance of emergency service in IMS and the delay time of the proposed model is analyzed.

Application of Hidden Markov Models in Speech Command Recognition

In this study,vector quantization and hidden Markov models were used to achieve speech command recognition.Pre-emphasis,a hamming window,and Mel-frequency cepstral coefficients were first adopted to obtain feature values.Subsequently,vector quantization and HMMs(hidden Markov models)were employed to achieve speech command recognition.The recorded speech length was three Chinese characters,which were used to test the method.Five phrases pronounced mixing various human voices were recorded and used to test the models.The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.

The Study and Application of the IoT in Pet Systems

The interaction between human and physical devices and devices in the real world is gaining more attention, and requires a natural and intuitive methodology to employ. According to this idea and living well, life has been a growing demand. Thus, how to raise pets in an easy way has been the main issue recently. This study examines the ability of computation, communication, and control technologies to improve human interaction with pets by the technology of the Internet of Things. This work addresses the improvement through the pet application of the ability of location-awareness, and to help the pet owners raise their pet on the activity and eating control easily. Extensive experiment results demonstrate that our proposed system performs significantly help on the kidney disease and reduce the symptoms. Our study not only presents the key improvement of the pet monitor system involved in the ideas of the Internet of Things, but also meets the demands of pet owners, who are out for works without any trouble.

Qualitative Analysis of a Fractional Pandemic Spread Model of the Novel Coronavirus (COVID-19)

In this study,we classify the genera of COVID-19 and provide brief information about the root of the spread and the transmission from animal(natural host)to humans.We establish a model of fractional-order differential equations to discuss the spread of the infection from the natural host to the intermediate one,and from the intermediate one to the human host.At the same time,we focus on the potential spillover of bat-borne coronaviruses.We consider the local stability of the co-existing critical point of the model by using the Routh–Hurwitz Criteria.Moreover,we analyze the existence and uniqueness of the constructed initial value problem.We focus on the control parameters to decrease the outbreak from pandemic form to the epidemic by using both strong and weak Allee Effect at time t.Furthermore,the discretization process shows that the system undergoes Neimark–Sacker Bifurcation under specific conditions.Finally,we conduct a series of numerical simulations to enhance the theoretical findings.

Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets(AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.

Lateral Migration and Nonuniform Rotation of Square Particle Suspended in Poiseuille Flow

A square particle suspended in a Poiseuille flow is investigated by using the lattice Boltzmann method with the Galilean-invariant momentum exchange method. The lateral migration of Segré-Silberberg effect is observed for the square particle, accompanied by the nonuniform rotation and regular wave. To compare with the circular particle, its circumscribed and inscribed squares are used in the simulations. Because the circumscribed square takes up a greater difference between the upper and lower flow rates, it reaches the equilibrium position earlier than the inscribed one. The trajectories of the latter are much closer to those of circle;this indicates that the circle and its inscribed square have a similar hydrodynamic radius in a Poiseuille flow. The equilibrium positions of the square particles change with Reynolds number and show a shape of saddle, whereas those of the circular particles are virtually not affected by Reynolds number. The regular wave and nonuniform rotation are owing to the interactions of the square shape and the parabolic velocity distribution of Poiseuille flow, and high Reynolds number makes the square rotating faster and decrease its oscillating amplitude. A series of contours illustrate the dynamic flow fields when the square particle has successive postures in a half rotating period. This study is beneficial to understand the motion of anisotropic particles and the dendrite growth in dynamic environment.

Low-Cost Posture Recognition of Moving Hands by Profile-Mold Construction in Cluttered Background and Occlusion

In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible oc-clusions. It goes for developing the untouchable low-complexity utility based on friendly hand-posture signaling. The scheme integrates the dominant temporal-difference detection, skin color detection and morphological filtering for efficient cooperation in constructing the hand profile molds. Those molds provide representative hand profiles for more stable posture recognition than accurate hand shapes with in effect trivial details. The resultant bounding box of tracking the signaling molds can be treated as a regular-type object-matched ROI to facilitate the stable extraction of robust HOG features. With such commonly applied features on hand, the prototype SVM is adequately capable of obtaining fast and stable hand postures recognition under natural hand movement and non-hand object occlusion. Experimental results demonstrate that our scheme can achieve hand-posture recognition with enough accuracy under background clutters that the targeted hand can be allowed with medium movement and palm-grasped object. Hence, the proposed method can be easily embedded in the mobile phone as application software.

The Design of a Cloud and Mobile Healthcare System

The aging society will become a serious problem for most countries in the world. Under the constraint of limited medical resource, the self-health management becomes important. In this paper, a mobile healthcare system is implemented. One can easily monitor his/her physiological data through the using of a smartphone that is wirelessly connected to different medical detection devices. A cloud database is established for storing and analysing these physiological data. The guidance of suitable physical exercises to individuals is then given in the system. This paper shows the details of the system implementation.

Blockchain-Enabled Mitigation Strategies for Distributed Denial of Service Attacks in IoT Sensor Networks:An Experimental Approach

Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various industries.However,IoT devices,typically low-powered,are susceptible to cyber threats.Conversely,blockchain has emerged as a robust solution to secure these devices due to its decentralised nature.Nevertheless,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security vulnerabilities.Blockchain,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT devices.This study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity threats.Integrating blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security vulnerabilities.This paper analyses potential security weaknesses in blockchain and their impact on network operations.We developed a real IoT test system utilising three prevalent blockchain applications to conduct experiments.The results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.

A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images.Automated analysis of medical images is essential for doctors,as manual investigation often leads to inter-observer variability.This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework.The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization(MIWPSO)and Fuzzy C-Means clustering(FCM)algorithms.Traditional FCM does not incorporate spatial neighborhood features,making it highly sensitive to noise,which significantly affects segmentation output.Our method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate noise.The results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image segmentation.Furthermore,segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule(DT-TAR)Algorithm.Comparative analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42%on the dataset,highlighting its significant impact on improving diagnostic capabilities in medical imaging.

Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems

Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive data.This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information systems.BERT’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing detection.Achieving an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing emails.This approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.

A Hybrid CNN-Brown-Bear Optimization Framework for Enhanced Detection of URL Phishing Attacks

Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial services.After the first reported incident in 1995,its impact keeps on increasing.Also,during COVID-19,due to the increase in digitization,there is an exponential increase in the number of victims of phishing attacks.Many deep learning and machine learning techniques are available to detect phishing attacks.However,most of the techniques did not use efficient optimization techniques.In this context,our proposed model used random forest-based techniques to select the best features,and then the Brown-Bear optimization algorithm(BBOA)was used to fine-tune the hyper-parameters of the convolutional neural network(CNN)model.To test our model,we used a dataset from Kaggle comprising 11,000+websites.In addition to that,the dataset also consists of the 30 features that are extracted from the website uniform resource locator(URL).The target variable has two classes:“Safe”and“Phishing.”Due to the use of BBOA,our proposed model detects malicious URLs with an accuracy of 93%and a precision of 92%.In addition,comparing our model with standard techniques,such as GRU(Gated Recurrent Unit),LSTM(Long Short-Term Memory),RNN(Recurrent Neural Network),ANN(Artificial Neural Network),SVM(Support Vector Machine),and LR(Logistic Regression),presents the effectiveness of our proposed model.Also,the comparison with past literature showcases the contribution and novelty of our proposed model.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.