A Sample Proposal Enhancing the Security of the Cloud Computing System Through Deep Learning and Data Mining

Malware Detection and Analysis play a primary role in information access points such as workstations, servers, cloud storage, and mobile devices. Securing information from malware is a challenging task, and existing anti-malware applications suffer from detecting new types of attacks. In recent years, machine learning-based malware detection techniques have been applied for effective malware detection and analysis for existing and new types of malware with few drawbacks such as misclassification and increased complexity. To overcome this, the chapter focuses on adopting deep learning (DL) models to build an anti-malware application via modern Artificial Intelligence (AI) techniques. Quantum Machine Learning (QML) is a research area that explores the interplay of ideas from quantum computing and machine learning. The proposed framework is built with various DeepNet Models to improve predicted accuracy, which can carry out universal quantum computations. QML performs the information processing with quantum computers relies on substantially different laws of physics called quantum theory. Initially, executable files will be converted to binary files and forwarded to DeepNet Models to multi-classify the novel variants of malware. A comprehensive survey is carried out on the implementation of ML/DL models for malware detection and analysis by considering various factors.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Artificial Intelligence (AI) has emerged as one of the most transformative technological developments of the modern era, offering a profound impact on various sectors, including education. The integration of AI into education presents a complex interplay of opportunities, challenges, and risks that require comprehensive exploration. This study adopts a mixed-methods approach, utilizing bibliometric, conceptual, and hermeneutic analyses to examine the role of AI in educational contexts. Bibliometric analysis quantitatively and visually maps research trends, employing tools to analyze 8,288 documents retrieved from the Scopus database using the keywords "Artificial Intelligence" OR "AI" AND "Education." Over the past two decades, the field has seen exponential growth, with contributions from 28,740 authors worldwide. The mapping highlights key areas of AI application, such as machine learning, deep learning, educational engineering, and computer-aided instruction, alongside technologies like ChatGPT, virtual reality, big data, and data mining. Conceptual analysis dissects critical ideas such as posthumanism, transhumanism, and their intersection with AI in education. These paradigms are reshaping education by challenging its traditional roles as a vehicle for human development and societal progress. AI's role in facilitating personalized learning experiences underscores its potential for enhancing efficiency and adaptability. However, it also raises pressing questions about its alignment with broader educational objectives. Beyond knowledge transfer, education fosters character development, critical thinking, and social skills areas potentially undermined by overreliance on AI technologies. The hermeneutic method provides a deeper interpretation of philosophical texts and literature, emphasizing the ethical and societal implications of integrating AI into education. Posthumanism and transhumanism, which advocate for merging human capabilities with technological advancements, pose existential challenges to education's foundational values. Tools like ChatGPT, while powerful, risk eroding academic integrity and critical skill development. For instance, reliance on AI for solving exam problems or answering complex queries undermines the principles of effort and intellectual growth, essential components of education. The research underscores the dual-edged nature of AI in education. On one hand, it enables adaptive and efficient learning systems. On the other hand, it disrupts traditional educational practices, raising ethical dilemmas and questions about the future of human-centric learning. Philosophical perspectives are crucial in navigating these disruptions, advocating for an education system that balances technological integration with the preservation of core academic values. This involves addressing the ethical considerations surrounding AI applications, particularly the potential misuse of tools like ChatGPT, which are byproducts of posthumanist and transhumanist ideologies. Education, as a transformative process, must evolve to integrate AI responsibly, ensuring that it complements rather than replaces human intellectual efforts. This requires reconstructing educational systems to mitigate the unintended consequences of AI technologies. Philosophical inquiry into the purpose of education must guide the realignment of its goals, emphasizing how knowledge is acquired and skills are nurtured in a rapidly evolving digital landscape. By fostering awareness of the ethical and practical implications of AI, educational institutions can reclaim their role in cultivating well-rounded individuals capable of critical thought and ethical decision-making. In conclusion, this study highlights the need for a balanced approach to integrating AI into education. While technological advancements offer unprecedented opportunities for innovation, they must not overshadow the humanistic values at the core of education. By addressing the challenges posed by posthumanism and transhumanism, the educational sector can harness AI's potential while safeguarding its mission to develop responsible, ethical, and capable individuals for the 21st century.

Aim/Purpose: This study carried out a systematic review of the literature on artificial intelligence (AI) in English language teaching (ELT). The objective was to delineate the current research progress in the field and to further understand the challenges. Background: The study analyzed articles published between 2015 and 2021. Methodology: The qualitative research method was employed. Five steps were taken to steer the review. 200 articles were scrutinized; 64 were retained. Contribution: Prior research on AI in ELT has not investigated how the literature is progressing or what areas of AI are being covered. Without a holistic picture, some important research findings could be missed. Understanding how studies on AI in ELT are designed and implemented will contribute to a greater understanding of the existing state of research. Findings: Findings show that there is a promising future for AI in ELT. AI in ELT yielded positive results in terms of optimizing the English language skills, translation, assessment, recognition, attitude, satisfaction, etc. It was also found out that more and more articles on the topic are being published; the mixed research method is the most commonly used, higher education level is the most sampled, students as participants are the most sampled, and most studies developed novel AI-based systems. Various AI approaches have been identified in the reviewed studies, including machine learning, neural network, support vector machine, genetic algorithms, deep learning, decision tree, expert system, natural language processing, data mining, cloud computing, and edge computing. However, AI in ELT is still in its infancy, where little research has been conducted and gaps in the literature are still present, especially in terms of inherent issues related to body language, gestures, expressions, emotions, translation, lack of elaborate description of teaching material used for learning driven by AI, uncertainties and vagueness with regards to what can be considered under the realm of AI, and most authors being outside of the ELT discipline. Recommendations for Practitioners: This literature review is likely to provide practitioners with an overview of the current adopted technology, research method, instruments and/or tools, educational level, language skill, and the effects reported by the AI-based studies for designing effective systems for the use of AI in their ELT classrooms. Recommendation for Researchers: Researchers need to conduct research on AI in ELT along with a detailed in-depth description of the methodology, research design, and the proposed systems used to achieve AI in ELT. Furthermore, it is recommended that researchers explore the efficiency of AI-based systems used in previous research and ensure their relevance and functionality. They are also required to provide in-depth analysis of the challenges inherent to systems that have been highlighted in the literature, which will maximize the potentials of these AI-based technologies. Impact on Society: The findings of this paper can provide visualization of research findings that could particularly benefit researchers, educators, and AI specialists who are involved in the study of the applications of AI in ELT. Future Research: Future AI research needs to seriously include more detailed descriptions of the method in further research.

A inovação tecnológica se mantém como relevante fator de crescimento econômico no contexto capitalista dinâmico, marcado pelo avanço das Tecnologias de Informação e Comunicação (TIC), no qual as organizações articulam capacidades dinâmicas na busca de vantagem competitiva sustentável, principalmente através de competências individuais. Nesse cenário, destacam-se a Indústria 4.0 (I4.0) e a Transformação Digital (TD), que inclui Big Data, Internet of Things (IoT) e Artificial Intelligence (AI), com ferramentas baseadas em algoritmos complexos, machine learning e deep learning. Podem servir à mineração de dados, análises preditivas e interação com pessoas através de Agentes Virtuais Inteligentes (AVI) que utilizam linguagem natural e aprendizado contínuo, formas de emular habilidades humanas complexas. Deste modo, os elementos da TD se apresentam como novas tecnologias que impactam a Organização do Trabalho (OT) e intensificam os desafios relacionados à integração da função estratégica da Gestão de Recursos Humanos (GRH), suas práticas e instrumentos, com exemplos de produtos (como HR analytics, people analytics e Artificial Intelligence, oferecidos por startups/spin-offs de HR Tech. Sob essas perspectivas, é proposto discutir o uso de IA (Computação Cognitiva) a partir do caso de um banco brasileiro, inicialmente através da percepção de seus funcionáros, atendidos por um AVI que presta informações sobre os produtos e serviços desta empresa, atividade normalmente atribuída aos atendentes do departamento de contact center. Como complemento, buscou-se observar se o uso dessa nova tecnologia, voltada a processos bancários, também inclui processos de RH.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

PurposeThe purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.Design/methodology/approachA large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.FindingsThe findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.Originality/valueThis investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

AI has rapidly changed how web applications are created and delivered in recent years. Using AI brings the user benefits of better automated processes through intelligent self-service, personalization of user experiences/customers, predictive analysis, and now includes real-time decision making. Before AI, web applications were mostly static or rules-based with limited or no change in response to how users interact with them. The introduction of advanced learning technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) has enabled web applications to become "intelligent" platforms that can receive input from a large volume of data and make adjustments in performance to continuously improve Keywords:Artificial Intelligence (AI), Web Applications, Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Recommendation Systems, Chatbots, Predictive Analytics, Data Mining.

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is limited by the widespread use of obfuscation. Dynamic analysis is not impacted by obfuscation but is defeated by ubiquitous anti-analysis techniques and requires more computational power. Sophisticated and evasive malware is challenging to extract authentic discriminatory features from and, combined with poor quality datasets, this can lead to a situation where a model achieves high accuracy with only one specific dataset.

Sentient tools--powered by incredible advances in artificial intelligence, deep learning, and data mining--represent the next stage of intelligent, aware, and social machines designed specifically to work with people. The Web extra at https://youtu.be/XtMFZ0sDzIk is an audio recording in which Science Fiction Prototyping editor Brian David Johnson talks with Richard Sear, vice president of consulting at Frost and Sullivan, about the next stage of intelligent, aware, and social machines designed specifically to work with people.

Recent advances in Internet of Things (IoT) technologies require a new type of IoT security environment. Various heterogeneous smart devices have easy access to IoT environment, and as the number of users increases, they are exposed to various threats such as malicious attacks on IoT devices and IoT infrastructure, and data tampering by malicious code. Malware detection in IoT requires data and models for continuous and changing learning of smart devices. Methods/Statistical analysis: To minimize these security threats, various malware detection techniques in the field of IoT security have been studied. Malware detection in IoT environment is important for data derivation and learning model required for continuous and changing learning of smart devices. The metadata of malware detection can be normalized by the value of device id, time, behavior, location and state. This paper proposes behavior-based malware detection using deep learning (BMD-DL). Findings: BMD-DL was able to collect metadata about behavior-based malicious behavior and learn and detect malicious codes through deep learning. In addition, through the learned model, IoT Security is provided by disconnecting malicious devices that cause malicious behavior in the IoT environment. Improvements/Applications: BMD-DL collects behavioral data generated from multiple devices in the IoT and applies the results learned through deep learning to detect persistent malware.

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial intelligence is used in predicting the clinical outcomes before minimally invasive treatments for benign prostatic hyperplasia, to address the insufficient reliability despite multiple assessment parameters, such as flow rates and symptom scores. Various models of artificial intelligence and its contemporary applications in benign prostatic hyperplasia are reviewed and discussed. A search strategy adapted to identify and review the literature on the application of artificial intelligence with a dedicated search string with the following keywords: “Machine Learning,” “Artificial Intelligence,” AND “Benign Prostate Enlargement” OR “BPH” OR “Benign Prostatic Hyperplasia” was included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. In the present review, 1600 patients were included from 4 studies that used different classifiers such as fuzzy systems, computer-based vision systems, and clinical data mining to study the applications of artificial intelligence in diagnoses and severity prediction and determine clinical factors responsible for treatment response in benign prostatic hyperplasia. The accuracy to correctly diagnose benign prostatic hyperplasia by Fuzzy systems was 90%, while that of computer-based vision system was 96.3%. Data mining achieved sensitivity and specificity of 70% and 50%, respectively, in correctly predicting the clinical response to medical treatment in benign prostatic hyperplasia. Artificial intelligence is gaining attraction in urology, with the potential to improve diagnostics and patient care. The results of artificial intelligence-based applications in benign prostatic hyperplasia are promising but lack generalizability of results. However, in the future, we will see a shift in the clinical paradigm as artificial intelligence applications will find their place in the guidelines and revolutionize the decision-making process.Keywords: Artificial intelligence; deep learning; machine learning; benign prostate enlargement; benign prostatic hyperplasia.