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Introdução: A Leucemia de Células Pilosas (Tricoleucemia) é uma rara neoplasia linfoproliferativa crônica de células B, corresponde a 2% de todas as leucemias. Em geral apresenta curso indolente e é caracterizada pela presença de linfócitos com projeções citoplasmáticas finas, conhecidas como células pilosas. Os sintomas mais comuns incluem fadiga, fraqueza, esplenomegalia, pancitopenia e infecções oportunistas recorrentes. O diagnóstico é baseado na morfologia das células em sangue periférico ou da medula óssea, que caracteristicamente apresentam projeções citoplasmáticas, e na positividade de marcadores CD11c, CD103, CD123 e CD25 pela imunofenotipagem. Objetivo: Relatar um caso de um paciente idoso com pancitopenia, sendo diagnosticado como portador de Leucemia de Células Pilosas. Relato de caso: Paciente de 80 anos, masculino, previamente hígido, sem comorbidades, internado por adinamia e constipação, exame físico sem esplenomegalia e o hemograma admissional evidenciou pancitopenia, com Hb de 10,2 g/dL; Leucócitos de 2330 /mm3 (Sg 1470/mm3 e Li 820/ mm3) e Plaquetas 27.000/mm3. O paciente realizou mielograma e imunofenotipagem para investigação diagnóstica. O mielograma revelou medula óssea hipocelular apresentando disteritropoiese discreta e predominância de linfócitos maduros, além de formas anômalas, sugerindo tricoleucócitos. A imunofenotipagem por citometria de fluxo revelou um perfil imunofenotípico positivo para marcadores de Leucemia de Células Pilosas: Kappa, CD19, IgM forte, CD20 forte, CD25, CD11c, CD103, CD123, CD79b forte, CD200, CD81 e CD45. O perfil imunofenotípico foi negativo para CD3, CD4, CD8, CD56, CD10, ROR1, CD5, CD11b, CD13, CD33, CD34, CD43, CD23 e Lambda. A conclusão foi compatível com o diagnóstico de Leucemia de Células Pilosas. Com estabilidade clínica e hemodinâmica, o paciente recebeu alta com encaminhamento ambulatorial para um serviço de referência em Hematologia. Discussão: A Tricoleucemia frequentemente se apresenta com pancitopenia devido à infiltração da medula óssea por células pilosas. Mais frequente em homens, e a partir da 5°década de vida, pode mimetizar ou coexistir com outras doenças hematológicas clonais e associar-se a desordens auto-imunes. O tratamento padrão para a doença inclui análogos de purina, como cladribina e pentostatina, que demonstram altas taxas de resposta e prolongam a sobrevida livre de doença. A combinação com rituximabe é uma abordagem terapêutica que vem ganhando destaque e sendo cada vez mais aplicada na prática clínica. Para pacientes que apresentam doença refratária ou recidivante, novas opções estão disponíveis, incluindo inibidores de BRAF, inibidores de MEK e terapias direcionadas. Resposta completa pode ser alcançada em 80 a 85% dos pacientes e mais de 90% tem sobrevida global estimada em 10 anos. Conclusão: A Leucemia de Células Pilosas de forma clássica é um distúrbio linfoproliferativo crônico e indolente. Diante de um paciente com pancitopenia, podemos estar diante de patologias variadas, clonais e não clonais, bem como reacionais, de base imunológicas ou como efeito de tratamentos variados. É imprescindível anamnese detalhada, laboratório e avaliação de medula óssea para excluir doenças neoplásicas.

Introdução: A Púrpura Trombocitopênica Idiopática (PTI) é uma doença autoimune caracterizada por trombocitopenia isolada, sem outras anormalidades hematológicas. A patogênese envolve a produção de autoanticorpos contra glicoproteínas na superfície das plaquetas, como GpIIb/IIIa e GpIb/IX, levando à destruição acelerada das plaquetas pelo sistema reticuloendotelial, sobretudo no território esplênico. Objetivo: Relatar o caso de um portador de PTI que evoluiu com hemorragia digestiva alta grave e refratariedade ao emprego de corticosteroides e imunoglobulina humana. Relato de caso: Paciente masculino, 24 anos, previamente hígido foi admitido no setor de emergência com quadro de hematêmese importante e foi submetido a intubação orotraqueal para proteção de via aérea. Em exames admissionais, foi constatado plaquetopenia (34 mil). Foi realizada transfusão de plaquetas, com piora da plaquetopenia (9 mil). Aventada a hipótese diagnóstica de PTI, iniciada pulsoterapia com Metilprednisolona 1g EV por 72 horas, sem resposta. Foi realizada nova pulsoterapia com Dexametasona 1g EV por 72 horas e novamente sem resposta, inclusive houve deterioração clínica importante com choque hemorrágico. Por fim, optado administrar Imunoglobulina Humana 1g/Kg EV por 48 horas. O paciente não apresentou melhora e houve queda da plaquetometria. Foi iniciado Eltrombopague 50 mg, com incremento plaquetário, entretanto o paciente cursou com hepatite medicamentosa secundária ao Eltrombopague. Suspensa terapia e, após normalização de enzimas hepáticas, reintroduzida a medicação (25 mg/dia). Posteriormente, paciente apresentou melhora clínica e laboratorial e recebeu alta hospitalar com 50 mil plaquetas, prescrição de Eltrombopague 25 mg/dia e Prednisona 30 mg/dia e retorno breve ao Hematologista. Discussão: A apresentação clínica da PTI pode incluir desde o surgimento de púrpura, equimose e, em casos mais graves, hemorragias mucocutâneas, trato gastrointestinal ou do sistema nervoso central. O diagnóstico desta condição é de exclusão, sendo assim necessário afastar condições que geram trombocitopenia, como infecções, neoplasias hematológicas e uso de medicamentos. O tratamento de primeira linha é a corticoterapia, podendo ser necessário o uso de imunoglobulina, agonistas da trombopoetina, anticorpo monoclonal como Rituximabe, outros imunossupressores e até mesmo esplenectomia. O Eltrombopague, utilizado no paciente acima, é um agonista do receptor de trombopoetina e interage com o domínio transmembrana do TPO-R e inicia cascatas de sinalização similares às da trombopoietina endógena, induzindo a proliferação e a diferenciação de megacariócitos provenientes das células progenitoras da medula óssea. Conclusão: A PTI pode ser considerada como leve à grave a depender das manifestações clínicas do paciente. O tratamento de primeira linha envolve o uso de corticosteroides, entretanto, em caso de resistência à esses, lança-se a mão de outras linhas de tratamento.

Objective The modern era of cognitive intelligence in clinical space has led to the rise of ‘Medical Cognitive Virtual Agents’ (MCVAs) which are labeled as intelligent virtual assistants interacting with users in a context-sensitive and ambient manner. They aim to augment users' cognitive capabilities thereby helping both patients and medical experts in providing personalized healthcare like remote health tracking, emergency healthcare and robotic diagnosis of critical illness, among others. The objective of this study is to explore the technical aspects of MCVA and their relevance in modern healthcare. Methods In this study, a comprehensive and interpretable analysis of MCVAs are presented and their impacts are discussed. A novel system framework prototype based on artificial intelligence for MCVA is presented. Architectural workflow of potential applications of functionalities of MCVAs are detailed. A novel MCVA relevance survey analysis was undertaken during March-April 2023 at Bhubaneswar, Odisha, India to understand the current position of MCVA in society. Results Outcome of the survey delivered constructive results. Majority of people associated with healthcare showed their inclination towards MCVA. The curiosity for MCVA in Urban zone was more than in rural areas. Also, elderly citizens preferred using MCVA more as compared to youths. Medical decision support emerged as the most preferred application of MCVA. Conclusion The article established and validated the relevance of MCVA in modern healthcare. The study showed that MCVA is likely to grow in future and can prove to be an effective assistance to medical experts in coming days.

Sentiment analysis (SA) is a popular method for obtaining relevant and subjective information from textual content. Sentiment analysis of multimedia material is helpful for various reasons but it is seen as challenging since the messages are often brief, unstructured, and contain linguistic inconsistencies. Previous research on sentiment analysis usually focused on dual or triple-class analysis while using older language modeling techniques. Furthermore, penta-class classification tasks have not been addressed as much. To deal with the challenge, we present a transformer-based model called BertSent that uses ordered preprocessing steps combined with transformer-based tokenization and optimization to get the best sentiment analysis results focused on dealing with limited data. Moreover, our framework handles the challenge of penta-class classification of tweets, and to that end, we combine many preprocessing techniques to fine-tune our transformer-based model. We employ resampling techniques to address class imbalance issues in the penta-class setup which improves model generalization and performance. For that purpose, we incorporate both over-sampling and under-sampling to tackle the challenge of class imbalance when dealing with the penta-class classification problem. Moreover, this article also compares the performance of the transformer-based model against a variety of deep learning-based models, including bi-directional models. The experimentations and results support our model’s remarkable performance considering the limited data and penta-class classification challenge. The results provide an interesting perspective as both under-sampling and oversampling provide similar results. BertSent model combined with over-sampling provides slightly better performance with 75.3% test accuracy in comparison to under-sampling which resulted in 75.1% accuracy.

Based on the results of this research, a new method for separating Arabic offline text is presented. This method finds the core splitter between the “Middle” and “Lower” zones by looking for sharp character degeneration in those zones. With the exception of script localization and the essential feature of determining which direction a starting point is pointing, the baseline also functions as a delimiter for horizontal projections. Despite the fact that the bottom half of the characteristics is utilized to differentiate the modifiers in zones, the top half of the characteristics is not. This method works best when the baseline is able to divide features into the bottom zone and the middle zone in a complex pattern where it is hard to find the alphabet, like in ancient scripts. Furthermore, this technique performed well when it came to distinguishing Arabic text, including calligraphy. With the zoning system, the aim is to decrease the number of different element classes that are associated with the total number of alphabets used in Arabic cursive writing. The components are identified using the pixel value origin and center reign (CR) technique, which is combined with letter morphology to achieve complete word-level identification. Using the upper baseline and lower baseline together, this proposed technique produces a consistent Arabic pattern, which is intended to improve identification rates by increasing the number of matches. For Mediterranean keywords (cities in Algeria and Tunisia), the suggested approach makes use of indicators that the correctness of the Othmani and Arabic scripts is greater than 98.14 percent and 90.16 percent, respectively, based on 84 and 117 verses. As a consequence of the auditing method and the assessment section’s structure and software, the major problems were identified, with a few of them being specifically highlighted.

Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the “Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection” dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL.

Missing values in the k-NN algorithm are a significant research concern, especially in low-grade tumours and CSF fluid, which are commonly identified in MRI scans. Missing values are usually ignored, but when data is mined, they can lead to bias and errors. In addition, the data is not missing at random. This study improves image accuracy, boosts the efficiency of missing k-NN hybrid values, and develops a research technique for detecting CSF fluid deposits in brain areas separated from non-tumor areas. We also offer a new method for detecting low-grade tumours or cerebrospinal fluid (CSF) formation in its early stages. In this study, we combine the hybrid K-Nearest Neighbor algorithm with the Discrete Fourier transform (DFT), as well as Time-Lagged analysis of four-dimensional (4D) MRI images. These dependencies exist in both space and time, but present techniques do not account for both sequential linkages and numerous types of missingness. To address this, we propose the DFLk-NN imputation method, which combines two imputation approaches based on a hybrid k-NN extension and the DFT to capture time-lag correlations both within and across variables. There are several types of missingness are enables the imputation of missing values across the variable even when all the data for a given time point is missing. The proposed method gives high accuracies of MRI datasets and retrieves the missing data in the images.

Crypto ransomware is a type of malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber security community due to several successful worldwide attacks. The encryption employed had caused irreversible damage to the victim’s digital files, even when the victim chooses to pay the ransom. Therefore, this research proposes the Pre-Encryption Detection Algorithm (PEDA) that can detect crypto-ransomware at the pre-encryption stage, when no encryption has been done. PEDA provides two levels of detection; the first level of detection was before the ransomware can be activated using a signature comparison with a known crypto-ransomware’s signature. The signature was generated using SHA-256 (Secure Hashing Algorithm) that allowed fast and accurate comparison of the file content. The second level of detection used Learning Algorithm (LA) that can detect crypto-ransomware based on pre-encryption application program interface (API). The LA produced a 100% recall rate based on 80:20 ratios of training and testing, and 99.9% recall rate with a 10-fold cross-verification test. In addition, this research had also successfully identified fourteen important APIs that can differentiate between ransomware and goodware. Three APIs were present in most ransomware, but less in goodware; these APIs were NtProtectVirtualMemory, NtResumeThread, and NtTerminateProcess. Eleven APIs, on the other hand, were mostly present in goodware, but less in ransomware; these APIs were NtWriteVirtualMemory, UuidCreate, NtDelayExecution, NtSetInformationFile, NtWriteFile, CreateThread, NtReadVirtualMemory, VirtualFreeEx, CreateDirectoryW, VirtualProtectEx, and SetFilePointer.

The scale of criminal networks (e.g. drug syndicates and terrorist networks) extends globally and poses national security threat to many nations as they also tend to be technologically advance (e.g. Dark Web and Silk Road cryptocurrency). Therefore, it is critical for law enforcement agencies to be equipped with the latest tools in criminal network analysis (CNA) to obtain key hidden links (relationships) within criminal networks to preempt and disrupt criminal network structures and activities. Current hidden or missing link predictive models that are based on Social Network Analysis models rely on ML techniques to improve the performance of the models in terms of predictive accuracy and computing power. Given the improvement in the recent performance of Deep Reinforcement Learning (DRL) techniques which could train ML models through self-generated dataset, DRL can be usefully applied to domains with relatively smaller dataset such as criminal networks. The objective of this study is to assess the comparative performance of a CNA hidden link prediction model developed using DRL techniques against classical ML models such as gradient boosting machine (GBM), random forest (RF) and support vector machine (SVM). The experiment results exhibit an improvement in the performance of the DRL model of about 7.4% over the next best performing classical RF model trained within 1500 iterations. The performance of these link prediction models can be scaled up with the parallel processing capabilities of graphical processing units (GPUs), to significantly improve the speed of training the model and the prediction of hidden links.

The sudden outbreak of the novel coronavirus (nCoV-19, COVID-19) and its rampant spread led to a significant number of people being infected worldwide and disrupted several businesses. With most of the countries imposing serious lockdowns due to the increasing number of fatalities, the social lives of millions of people were affected. Although the lockdown led to an increase in network activities, online shopping, and social network usage, it also raised questions On the mental wellness of society. Interestingly, excessive usage of social networks also witnessed humor traveling across the Internet in the form of Internet Memes during the lockdown period. Humor is known to affect our well-being, decision-making, and psychological systems. In this paper, we have analyzed the Internet Meme activity in Social Networks during the COVID-19 Lockdown period. As humor is known to relieve individuals from psychological stress, it is necessary to understand how human beings adopted Internet Memes for coping up with the lockdown stress and stress-relieving mechanism during the lockdown period. In this paper, we have considered thirty popular memes and the increase in the number of their captions within the period (September 2017 to August 2020). An increase in Internet Meme activity since the lockdown period (March 2020) depicts an increase in online social behavior. We analyze the internet meme activity in social networks during the COVID-19 lockdown period using random forest, multi-layer perceptron, and instance-based learning algorithms followed by data visualization using line graph and Heat Map (8 & 15 clustered). We also compared the performance of the models using evaluation parameters like mean absolute error, root-mean-squared error & Kappa statistics and observed that random forest and instance-based learning algorithms perform better than multi-layer perceptrons. The result indicates that random forest and instance-based learning classifiers are having near perfect classification tendencies whereas multi-layer perceptrons showed around 97% classification accuracy.

The accurate diagnosis of the initial stage COVID-19 is necessary for minimizing its spreading rate. The physicians most often recommend RT-PCR tests; this is invasive, time-consuming, and ineffective in reducing the spread rate of COVID-19. However, this can be minimized by using noninvasive and fast machine learning methods trained either on labeled patients’ symptoms or medical images. The machine learning methods trained on labeled patients’ symptoms cannot differentiate between different types of pneumonias like COVID-19, viral pneumonia, and bacterial pneumonia because of similar symptoms, i.e., cough, fever, headache, sore throat, and shortness of breath. The machine learning methods trained on labeled patients’ medical images have the potential to overcome the limitation of the symptom-based method; however, these methods are incapable of detecting COVID-19 in the initial stage because the infection of COVID-19 takes 3 to 12 days to appear. This research proposes a COVID-19 detection system with the potential to detect COVID-19 in the initial stage by employing deep learning models over patients’ symptoms and chest X-Ray images. The proposed system obtained average accuracy 78.88%, specificity 94%, and sensitivity 77% on a testing dataset containing 800 patients’ X-Ray images and 800 patients’ symptoms, better than existing COVID-19 detection methods.

When it comes to smart cities, one of the biggest issues is energy optimization. This is because these cities employ a large number of interconnected devices to autonomously manage city operations, which consumes a lot of energy. This difficulty has been addressed in this paper by using the advantages of contemporary cutting-edge technologies such as the Internet of Things (IoT), 5 G, and cloud computing for energy efficiency in smart cities. With the use of these cutting-edge technologies, we have proposed a model that can be used to optimize energy consumption in smart homes and smart cities alike. Street lighting, building and street billboards, smart homes, and smart parking are among the four essential features of smart cities that would benefit from the proposed model’s energy savings. All smart city electric appliances will be equipped with IoT sensors that will detect movements and react to commands. In order to transport data swiftly between communication channels and the cloud, 5 G technology will be deployed, and the cloud technology will be used to store and retrieve data effectively. The suggested model was evaluated using mathematical modeling, and the findings indicate that the proposed model may assist in improving energy usage in smart cities.

The importance of smart card based two factor authentication can be gauged by the fact that many schemes have been proposed so far yet none of them used a systematic framework to critically analyze themselves to prove them practical and use worthy. This research however accomplishes exactly this by showing how using a criteria set can aid in highlighting the underlying hidden weaknesses in the design of the proposed schemes and what can be done to improve them by incorporating security features from the initial development stages of the protocols. This would also ensure only meaningful contribution in developing stronger schemes. Our research also lays the foundation stone by assessing the latest scheme proposed by Xie et al. and suggesting improvements in it. And finally, a novel scheme rating mechanism has been introduced to rate schemes thus helping in determining the actual goodness of the schemes and facilitating top managements of corporate sectors to make informed decisions.

Background The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge. Methods This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node. Results Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.

Internet of things architecture is the integration of real-world objects and places with the internet. This booming in technology is bringing ease in our lifestyle and making formerly impossible things possible. Internet of things playing a vital role in bridging this gap easily and rapidly. IoT is changing our lifestyle and the way of working the technologies, by bringing them together at the one page in several application areas of daily life. However, IoT has to face several challenges in the form of cyber scams, one of the major challenges IoT has to face is the likelihood of Ransomware attack. Ransomware is a malicious kind of software that restricts access to vital information in some way and demand payment for getting access to this information. The ransomware attack is becoming widespread daily, and it is bringing disastrous consequences, including loss of sensitive data, loss of productivity, data destruction, and loss of reputation and business downtime. Which further leads to millions of dollar daily losses due to the downtime. This is inevitable for organizations to revise their annual cybersecurity goals and need to implement proper resilience and recovery plan to keep business running. However, before proceeding towards providing a practical solution, there is a need to synthesize the existing data and statistics about this crucial attack to make aware to the researchers and practitioners. To fill this gap, this paper provides a comprehensive survey on evolution, prevention and mitigation of Ransomware in IoT context. This paper differs from existing in various dimensions: firstly, it provides deeper insights about Ransomware evolution in IoT. Secondly; it discusses diverse aspects of Ransomware attacks on IoT which include, various types of Ransomware, Current research in Ransomware, Existing techniques to prevent and mitigate Ransomware attacks in IoT along with the ways to deal with an affected machine, the decision about paying the ransom or not, and future emerging trends of Ransomware propagation in IoT. Thirdly, a summary of current research is also provided to show various directions of research. In sum, this detailed survey is expected to be useful for researchers and practitioners who are involved in developing solutions for IoT security.

Internet of medical things (IoMT) is getting researchers' attention due to its wide applicability in healthcare. Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes. Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers. We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature. This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered. A taxonomy is presented to categorize the schemes. Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio. We present a summary of these scheme along with analytical discussion. Finally, a number of open research challenges are identified. Moreover, the schemes are explored to identify the challenges that are addressed in each scheme.

Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node (FN), respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the FN minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.

Cracked concrete is a problem due to several factors such as poor maintenance, insufficient reinforcement, or steel corrosion leading to crack propagation. There is a need to increase the load-bearing capacity of concrete slabs and increase their life span. The use of waste polyethylene terephthalate fibres in concrete can dramatically alleviate the problem of crack propagation and failure sustainably. Furthermore, the utilization of waste plastic in this manner is environmentally friendly. This study presents the experimental investigation into the mechanical strength properties of concrete with respect to the effect of various mass fractions of polyethylene terephthalate fibre. The polyethylene terephthalate fibres were added at mass fraction of 0.5%, 1.0%, 1.5%, and 2.0%. An experimental investigation was carried out to explore the effect of varying fibre mass fractions on the slump value, rebound number, split tensile strength, flexural strength, and compressive strength. An increase in flexural strength, rebound number and compressive strength was noted with an increase in fibre mass fraction. However, a decrease in split tensile strength was noted. The addition of 0.5% fibre gave the highest compressive and flexural strength of 29.32 N/mm2 and 28 N/mm2, respectively. However, the addition of fibre lowered the split tensile strength beyond the control specimen at all fibre mass fractions. The experimental results of this study indicate that the addition of polyethylene terephthalate fibre enhances the mechanical strength of concrete at low fibre mass fraction percentages. The PET fibre reinforced concrete is suitable for use in paving and ceiling slabs at a fibre addition of 0.5% for optimum workability and mechanical strength.

Objetivo: Relatar as experiências de educação em saúde dos residentes multiprofissionais em Oncohematologia. Metodologia: Estudo do tipo relato de experiência. A atividades ocorreram no mês de setembro de 2020 e nos meses de junho de 2021 durante o período de pandemia. Os locais em que as atividades aconteceram foi: Ambulatório de Quimioterapia no Centro de Hematologia e Hemoterapia do Ceará e Hospital Universitário Walter Cantídio. O público atingido foram os pacientes e acompanhantes que aguardavam atendimento no ambulatório e funcionários do hospital. As atividades foram alusivas ao Setembro Verde - Doação de Medula Óssea e Junho Vermelho – Promoção da Doação de Sangue. Ocorreram nos turnos manhã e tarde, foi utilizado decoração do ambiente alusiva a temática, jogos como mitos e verdades, dado de perguntas e respostas, banner expositivo, entrega de folder. Resultados: Durante o período de pandemia as instituições que prestam serviços em oncologia e hospitais prestaram assistência ininterrupta, sendo serviços essenciais. Cuidados como higienização das mãos, e distanciamento foram efetivados durante a realização das atividades. Ao todo 96 pessoas foram atingidas, sendo 27 pessoas em setembro de 2020, 66 profissionais de saúde em junho de 2021. Foi realizada introdução da temática com o público, sendo realizado tira dúvidas, sendo possível sensibilizar a população para a doação voluntária de medula óssea e sangue. Conclusões: Atividades de promoção da saúde como as descritas neste trabalho são de suma importância, pois divulgam conhecimento dos assuntos abordados nas atividades para a público, contribuindo para a efetivação de políticas públicas em saúde.

INTRODUCTION: The fact of our lives is that we will be reaching to elderly life stage sooner or later. Our elderly community has been left far behind of new technology updates. Technology advances just as age is catching up on us. To many who are in their twilight years, using cutting edge technologies may be a hurdle in their activities of daily living (ADLs).OBJECTIVES: Therefore, this study aims to identify and measure the ADL challenges that the elderly encounter and improve their quality of life (QoL).METHODS: This research embarked on semi-structured interviews at 9 geriatric care centres in Malaysia to investigate the ADL challenges by the elderly residents. The thematic analysis approach was employed for data analysis and further discussion.RESULTS: The research findings suggested that the QoL of the elderly is limited by the challenges of geriatric issues, poor living conditions, and technology acceptance barriers.CONCLUSION: In conclusion, the current research provides an overview of the ADL challenges faced by the elderly with recommendations of user-centred Internet of Things (IoT) devices for elderly to use in an ambient assisted living (AAL) environment in Society 5.0.

Climate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timing of growing and reproduction might be an essential tactic by which plant life can avoid frost. Flowers are more sensitive to hoarfrost than leaves but more sensitive to frost in most cases. In most cases, frost affects the size of the plant, its growth, and the production of seeds. In this article, we examined that how frost affects plants and flowers? How it affects the roots and prevents the growth of plants, vegetables, and fruits? Furthermore, we predicted how the frost will grow and how we should take early precautions to protect our crops? We presented the convolutional neural network model framework and used the conv1d algorithm to evaluate one-dimensional data for frost event prediction. Then, as part of our model contribution, we preprocessed the data set. The results were comparable to four weather stations in the United States. The results showed that our convolutional neural network model configuration is reliable.

Evidently, criminal network activities have shown an increasing trend in terms of complexity and frequency, particularly with the advent of social media and modern telecommunication systems. In these circumstances, law enforcement agencies have to be armed with advance criminal network analysis (CNA) tools capable of uncovering with speed, probable key hidden relationships (links/edges) and players (nodes) in order to anticipate, undermine and cripple organised crime syndicates and activities. The development of link prediction models for network orientated domains is based on Social Network Analysis (SNA) methods and models. The key objective of this research is to develop a link prediction model that incorporates a fusion of metadata (i.e. environment data sources such as arrest warrants, judicial judgement, wiretap records and police station proximity) with a time-evolving criminal dataset in order to be aware of real-world situations to improve the quality of link prediction. Based on the review of related work, most of the models are constructed by leveraging on classical machine learning (ML) techniques such as support vector machine (SVM) without metadata fusion. The problem with the use of classical ML techniques is the lack of available domain dataset which is sufficiently large for training purpose. Compared to sociaI network, criminal network dataset by nature tends to relatively much smaller. In view of this, deep reinforcement learning (DRL) technique which could improve the training of models with the self-generated dataset is leveraged upon to construct the model. In this research, a purely time-evolving DRL model (TDRL-CNA) without metadata fusion is designed as a baseline for comparison with the metadata fusion model (FDRL-CNA). The experimental results show that the predictive accuracy of new and recurrent links by the FDRL-CNA model is higher than the baseline TDRL-CNA model that does not factor data fusion from different data sources.

The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling.

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research.

In the Internet of Things (IoT), the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is susceptible to several disruptive attacks based on its functionalities and features. Among various RPL security solutions, a trust-based security is easy to adapt for resource-constrained IoT environments. In the existing trust-based security for RPL routing attacks, nodes’ mobility is not considered or limited to only the sender nodes. Similarly, these trust-based protocols are not evaluated for mobile IoT environments, particularly regarding RPL attacks. Hence, a trust and mobility-based secure routing protocol is proposed, termed as SMTrust, by critically analysing the trust metrics involving the mobility-based metrics in IoT. SMTrust intends to provide security against RPL Rank and Blackhole attacks. The proposed protocol is evaluated in three different scenarios, including static and mobile nodes in an IoT network. SMTrust is compared with the default RPL objective function, Minimum Rank with Hysteresis Objective Function (MRHOF), SecTrust, DCTM, and MRTS. The evaluation results indicate that the proposed protocol outperforms with respect to packet loss rate, throughput, and topology stability. Moreover, SMTrust is validated using routing protocol requirements analysis to ensure that it fulfils the consistency, optimality, and loop-freeness.

Attendance monitoring system is an essential element in all organizations and is considered as an integral part of efficient organizational information systems. Most of the time it so happens that if any stakeholder wants to meet concerned in charge employees they need to take an appointment and then consult them. In situations where meeting becomes inevitable there is no system available to trace them with in the vicinity of the organization. The current research has been undertaken to address this issue, which is to know whereabouts of in charge employees during organizational timings. The proposed system works on RFID technology where in, as the concerned employees enters in the organization vicinity, his RFID will be validated with a check from the organizational database, if he/she is indeed an employee of the respective organization he/she will be allowed to enter into workspace and his attendance will be updated in the respective organizational database. Their status will be displayed on the screens available outside the workspace; apart from it the details will also be updated on cloud and the stakeholders willing to meet their respective personnel can easily see from the respective web-portal and can make an appointment based on their availability.

Summary: Background: Maternal and neonatal mortality is high in Africa, but few large, prospective studies have been done to investigate the risk factors associated with these poor maternal and neonatal outcomes. Methods: A 7-day, international, prospective, observational cohort study was done in patients having caesarean delivery in 183 hospitals across 22 countries in Africa. The inclusion criteria were all consecutive patients (aged ≥18 years) admitted to participating centres having elective and non-elective caesarean delivery during the 7-day study cohort period. To ensure a representative sample, each hospital had to provide data for 90% of the eligible patients during the recruitment week. The primary outcome was in-hospital maternal mortality and complications, which were assessed by local investigators. The study was registered on the South African National Health Research Database, number KZ_2015RP7_22, and on ClinicalTrials.gov, number NCT03044899. Findings: Between February, 2016, and May, 2016, 3792 patients were recruited from hospitals across Africa. 3685 were included in the postoperative complications analysis (107 missing data) and 3684 were included in the maternal mortality analysis (108 missing data). These hospitals had a combined number of specialist surgeons, obstetricians, and anaesthetists totalling 0·7 per 100 000 population (IQR 0·2–2·0). Maternal mortality was 20 (0·5%) of 3684 patients (95% CI 0·3–0·8). Complications occurred in 633 (17·4%) of 3636 mothers (16·2–18·6), which were predominantly severe intraoperative and postoperative bleeding (136 [3·8%] of 3612 mothers). Maternal mortality was independently associated with a preoperative presentation of placenta praevia, placental abruption, ruptured uterus, antepartum haemorrhage (odds ratio 4·47 [95% CI 1·46–13·65]), and perioperative severe obstetric haemorrhage (5·87 [1·99–17·34]) or anaesthesia complications (11·47 (1·20–109·20]). Neonatal mortality was 153 (4·4%) of 3506 infants (95% CI 3·7–5·0). Interpretation: Maternal mortality after caesarean delivery in Africa is 50 times higher than that of high-income countries and is driven by peripartum haemorrhage and anaesthesia complications. Neonatal mortality is double the global average. Early identification and appropriate management of mothers at risk of peripartum haemorrhage might improve maternal and neonatal outcomes in Africa. Funding: Medical Research Council of South Africa.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

Introduction: Dendritic cells (DCs) have been recently employed as carriers for vaccines against several viral infections. The present study was designed to develop a prophylactic vaccine against hepatitis C virus (HCV) using DCs treated with Berberis vulgaris root extract (BRE), as a preclinical study. Methods: BRE was prepared and injected to female BALB/c mice for DCs expansion. The expanded splenocyte cells (EDC) were purified and efficiently loaded ex vivo with HCV-nonstructural protein 3, NS3, (EDC-NS3). Mice were subcutaneously inoculated with EDC-NS3 vaccine candidate thrice with 4-week-intervals and IL-12, IFN-γ, IL-4, IL-10, MHC class II, CD3, CD16, indoleamine 2, 3 dioxygenase (IDO) and total protein levels were measured, post-vaccination by PCR and flow cytometry. Moreover, cytotoxic T lymphocyte and humoral immune responses were examined. Results: Our data revealed that immunization with EDC-NS3 vaccine elevated IL-12, IFN-γ and IL-4 expressions as well as MHC II and CD16 at protein levels. It also elicited strong HCV-NS3-specific humoral and cellular immune responses. However, the expressions of CD3, IDO, and IL-10 were down-regulated, post-vaccination. Conclusion: EDC-NS3 immunization serves as an innovative modality for immunoprophylaxis against HCV infection.

Ransomware encrypts victim’s files or locks users out of the system. Victims will have to pay the attacker a ransom to decrypt and regain access to the user files. Petya targets individuals and companies through email attachments and download links. NotPetya has worm-like capabilities and exploits EternalBlue and EternalRomance vulnerabilities. Protection methods include vaccination, applying patches, et cetera. Challenges faced to combat ransomware includesocial engineering, outdated infrastructures, technological advancements, backup issues, and conflicts of standards. ThreeLevel Security (3LS) is a solution to ransomware that utilizes virtual machines along with browser extensions to perform ascan, on any files that the user wishes to download from the Internet. The downloaded files would be sent over a cloud server relay to a virtual machine by a browser extension. Any changes to the virtual machine after downloading the file would be observed, and if there were a malfunction in the virtual machine, the file would not be retrieved to the user’s system.

Criminal networks analysis has attracted several numbers of researchers as network analysis gained its popularity among professionals and researchers. In this study, we have presented a comprehensive review of community detection methods based on graph analysis. The concept of community was vividly discussed as well as the algorithms for detecting communities within a network. Broad categorization of community detection algorithms was also discussed as well as a thorough review of detection algorithms which has been developed, implemented and evaluated by several authors in social network analysis. Most importantly, a strict review of researches based on the detection of a community in a criminal network was carried out revealing the strength and limitations of criminal network community detection methods. Thus, itbecomes obvious through this study that more research activities is necessary and expected in order to further grow this research area.

Nitrate, the form of nitrogen often associated with kelp growth, is typically low in summer during periods of high macroalgal growth. More ephemeral, regenerated forms of nitrogen, such as ammonium and urea, are much less studied as sources of nitrogen for kelps, despite the relatively high concentrations of regenerated nitrogen found in the Southern California Bight, where kelps are common. To assess how nitrogen uptake by kelps varies by species and nitrogen form in southern California, USA, we measured uptake rates of nitrate, ammonium, and urea by Macrocystis pyrifera and Eisenia arborea individuals from four regions characterized by differences in nitrogen availability-Orange County, San Pedro, eastern Santa Catalina Island, and western Santa Catalina Island-during the summers of 2021 and 2022. Seawater samples collected at each location showed that overall nitrogen availability was low, but ammonium and urea were often more abundant than nitrate. We also quantified the internal %nitrogen of each kelp blade collected, which was positively associated with ambient environmental nitrogen concentrations at the time of collection. We observed that both kelp species readily took up nitrate, ammonium, and urea, with M. pyrifera taking up nitrate and ammonium more efficiently than E. arborea. Urea uptake efficiency for both species increased as internal percent nitrogen decreased. Our results indicate that lesser-studied, more ephemeral forms of nitrogen can readily be taken up by these kelps, with possible upregulation of urea uptake as nitrogen availability declines.

The rapid growth of the Internet of Things (IoT) and the massive propagation of wireless technologies has revealed recent opportunities for development in various domains of real life, such as smart cities and E-Health applications. A slight defense against different forms of attack is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed in transmission during data packet routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. In this research, we propose a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. This mainly detects, mitigates, and isolates attacks in RPL networks. The detection is based on a comparison of the rank strategy. The mitigation uses threshold and attack status tables, and the isolation adds them to a blacklist table and alerts nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies, such as Standard RPL with Attacks, Sink-Based Intrusion Detection Systems (SBIDS), and RPL+Shield. The analysis results showed that the SRPL-RP achieved significant improvements with a Packet Delivery Ratio (PDR) of 98.48%, a control message value of 991 packets/s, and an average energy consumption of 1231.75 joules. SRPL-RP provided a better accuracy rate of 98.30% under the attacks.

The early evolution of a supernova (SN) can reveal information about the environment and the progenitor star. When a star explodes in vacuum, the first photons to escape from its surface appear as a brief, hours-long shock-breakout flare1,2, followed by a cooling phase of emission. However, for stars exploding within a distribution of dense, optically thick circumstellar material (CSM), the first photons escape from the material beyond the stellar edge and the duration of the initial flare can extend to several days, during which the escaping emission indicates photospheric heating3. Early serendipitous observations2,4 that lacked ultraviolet (UV) data were unable to determine whether the early emission is heating or cooling and hence the nature of the early explosion event. Here we report UV spectra of the nearby SN 2023ixf in the galaxy Messier 101 (M101). Using the UV data as well as a comprehensive set of further multiwavelength observations, we temporally resolve the emergence of the explosion shock from a thick medium heated by the SN emission. We derive a reliable bolometric light curve that indicates that the shock breaks out from a dense layer with a radius substantially larger than typical supergiants.

Objective: To identify the different types of plasma cells in multiple myeloma. To evaluate the survival of patients according to plasma cell morphology. Method: Cytological aspects of plasma cell were examined according three criteria: the nucleolus, the chromatin and the nuclear-cellular ratio (N/C) of each plasma cell. Each plasma cell was identifid by the letter P followed by 3 digits. This identifiation allowed to classify them as immature plasma cells, intermediate plasma cells and mature plasma cells. The proportions of various plasma cells among patients, were used to determine their group of survival according to the algorithm of morphology of Goasguen. Results: Morphological aspects of plasma cells of 55 cases of multiple myeloma from 2004 to 2010 were analyzed in two laboratories in Kinshasa. The results gave the rate of eight different types of plasma cell, the rate of multiple myeloma of plasmablastic cells, increased levels of aberrant and immature plasma cells. The integration of these aberrant and immature plasma cells in the defiitions of survival groups showed that the group of good responders represented 38.2 % of patients. The intermediate group had 32.7%. While the group of poor responders counted 29.1%. Conclusion: We found a great number of aberrant and immature plasma cells. We reached that the majority of our patients were found in the intermediate group and the group of bad responders. This regrouping explains the aggressive character of the multiple myeloma among our patients

Objectifs : Déterminer la part de différentes entités d’hémopathies malignes myéloïdes et identifir leurs caractères cytologiques et épidémiologiques. Méthodes: L’étude s’est déroulée de 2004 à 2010 au sein de deux laboratoires de biologie médicale de Kinshasa. Après la ponction médullaire à l’aiguille intramusculaire ou spinale, les frottis des patients sont colorés au May Grunwald Giemsa. Les hémopathies myéloïdes malignes sont retenues, les variables cytologiques et épidémiologiques des patients sont étudiées. Résultats : Soixante dix neuf hémopathies malignes du tissu myéloïde sont diagnostiquées sur un total de 483 malades ponctionnés. Elles sont réparties chez 52 hommes et 27 femmes. Ces hémopathies atteignent tous les âges. Leur distribution selon les tranches d’âge montrait une courbe ascendante avec trois paliers : la vingtaine, la quarantaine et la soixantaine. Les entités les plus rencontrées étaient: 39,2 % des leucémies aiguës myéloblastiques (LAM) ; 35,4 % de leucémie myéloïde chronique (LMC) et 24,1 % des syndromes myélodysplasiques (SMD). La LMC prédominait entre 20-39 ans ; les SMD et quelques LAM, entre 50-59 ans. Au-delà de 60 ans, on avait le grand pic des LAM et le second pic de la LMC. Conclusion : Les hémopathies malignes myéloïdes présentaient une tendance à l’augmentation progressive sur la durée de l’étude. Les différentes entités apparaissaient plus tôt qu’à l’âge décrit dans la littérature avec un taux de SMD plus important.

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious program would be captured and analyzed using the learning algorithm (LA). The LA can determine whether the suspicious program was a crypto-ransomware or not, through API pattern recognition. This approach was used to ensure the most comprehensive detection of both known and unknown crypto-ransomware, but it may have a high false positive rate (FPR). If the prediction was a crypto-ransomware, PEDA would generate a signature of the suspicious program, and store it in the signature repository, which was in Phase-II. In PEDA-Phase-II, the signature repository allows the detection of crypto-ransomware at a much earlier stage, which was at the pre-execution stage through the signature matching method. This method can only detect known crypto-ransomware, and although very rigid, it was accurate and fast. The two phases in PEDA formed two layers of early detection for crypto-ransomware to ensure zero files lost to the user. However in this research, we focused upon Phase-I, which was the LA. Based on our results, the LA had the lowest FPR of 1.56% compared to Naive Bayes (NB), Random Forest (RF), Ensemble (NB and RF) and EldeRan (a machine learning approach to analyze and classify ransomware). Low FPR indicates that LA has a low probability of predicting goodware wrongly.

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.