Advances in AI for Financial, Cyber, and Healthcare Analytics: A Multidisciplinary Approach

This chapter will introduce financial analytics very holistically and then dive into how machine learning transformed the finance industry. The discussion shall start with the underlying principles of financial analytics; it involves rigorous analytical expositions of financial data to deduce insights that promote decision-making, enhance performance, and avoid risks. Areas such as performance analysis, risk management, forecasting, fraud detection, and optimization will be featured within this light theme of data-driven decision-making in contemporary finance. The role of machine learning is then discussed within the chapter. The author states that the impact machine learning has on predictive analytics, algorithmic trading, fraud detection, portfolio optimization, and scoring credit has increased lately. Much more accurate and almost instantaneous decision-making with financial applications is enabled by machine learning when processing large, complex datasets. A challenge to financial data, the chapter goes on to discuss issues it poses in terms of quality, non-stationarity, imbalanced datasets, and interpretability of model outcomes. While such challenges are plentiful, there are many opportunities as well inside this landscape, from alternative sources of data to real-time analytics, automation, and even RegTech solutions. The chapter concludes by stating that it is only when the following challenges are addressed that machine learning will truly be leveraged in finance for both scalable insight and cooperative intelligence.

Human Activity Recognition (HAR) plays a critical role in segregating and distinguishing human actions among data generated from videos and other numerous sensing modalities, such as accelerometer, gyroscope, GPS, and magnetometer. HAR is considered a rapidly growing field that has revolutionized numerous areas, such as healthcare, manufacturing, security, smart homes, etc. Manual extraction of features in traditional machine learning approaches makes it difficult to handle the spatial and temporal complexities of real-world datasets, thereby necessitating the need for Deep Learning algorithms that offer automatic feature extraction to effectively capture both the spatial and temporal data. This chapter provides a review of Deep Learning models for HAR, focusing on advancements in CNN and LSTM and their variant architectures that play a significant role in handling complex and multivariate datasets gathered from wearable devices and smartphones. Furthermore, attention mechanisms, such as the self-attention and squeeze and excitation modules, have significantly enhanced model performance by focusing on relevant feature maps and recalibrating them adaptively. These mechanisms do not only improve the accuracy but also the interpretability of the model by concentrating on the important aspects of the data in consideration. This chapter also highlights hybrid models that combine CNN and LSTM and their variants for more accurate HAR, especially when working with sensor-based datasets. Additionally, it also examines that incorporation of attention mechanisms not only boosts accuracy but also optimizes the complexity of the models. Key trends in attention-driven deep learning methods are examined, indicating their growing importance in real-world human activity recognition applications.

An extensive comparison of cutting-edge image processing and machine learning methods for leukemia identification and classification is presented in this chapter. The proposed approach incorporates a thorough, extensive analytical approach that includes morphological operations, watershed segmentation, Wiener filtering, Kmeans clustering, and Gaussian filtration. By fine-tuning microscopic medical images, these preprocessing techniques enable accurate feature extraction and categorization. Using a multi-class Support Vector Machine (SVM) model, a total of 20 sub-features, including morphological, visual, and statistical characteristics, are retrieved and categorized. The proposed approach effectively classifies Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), and normal cells with a detection accuracy of 97.06% when tested on a dataset of 250 samples. To demonstrate the efficacy of the proposed approach, it is compared to state-of-the-art methods. The current method achieves 91.2% accuracy for ALL. By providing a scalable, effective, and precise method for leukemia identification and categorization, this study discusses advanced computer-aided diagnostic tools. The findings highlight how machine learning and image processing technology may enhance medical diagnostics by helping medical practitioners identify leukemia subtypes early and accurately.

This chapter will explore the ethical implications of AI decision-making in domains from healthcare and finance to criminal justice and employment. Integrating AI technologies into daily operations has many benefits, increasing accuracy and efficiency and yielding data-driven insights. Some problems brought forth by this report include algorithmic bias that can lead to discriminatory action against minority groups. The greater concern that is surfacing today involves accountability and trust since the users and affected parties often lack a clear understanding in order to argue against the rationale of the automated decisions. Furthermore, where AI is at the crossroads of numerous human rights concerns, for instance, invasion of privacy and potential debasement of civil liberties, society faces direct challenges. AI models with comprehensive and well-curated datasets demonstrate diagnostic accuracy rates above 90%, whereas poor-quality data-derived models were incapable of performing adequately. Suggestions for developing ethical frameworks with fairness, accountability, and transparency in AI systems are included in the paper's conclusion. 

Anticipating cyber threats using Artificial Intelligence’s (AI) predictive learning is a proactive and innovative strategy for protecting system against major attacks. By using algorithms and data analysis techniques to detect and handle possible threats, the Artificial Intelligence merger renders security. Artificial Intelligence systems can manage cyber risks by recognizing patterns and raising alerts for unidentified dangers. Known attacks can be tackled using signature-based identification, which is a reliable approach for managing them. Real-time monitoring, data collection, preprocessing, and model training techniques are the features that have been incorporated into the suggested framework. Threat prediction skills are enhanced by Machine Learning algorithms, anomaly detection, and behavioral analysis.

Furthermore, by combining threat intelligence with continuous learning, Artificial Intelligence systems are sanctioned to adapt dynamically to the futuristic and evolving landscape of cyber threats. It guarantees a robust shield for private information, proactively identifying vulnerabilities and mitigating risks while simultaneously reinforcing public confidence in the reliability and security of digital systems. These advanced capabilities enable early detection of potential threats and proactive responses to safeguard private and sensitive data effectively. The use of Artificial Intelligence in cyber security goes beyond traditional reactive measures by providing real-time insights and automated solutions that aim to mitigate both known and unknown emerging threats. This adaptive and innovative strategy provides cyber defenses, providing enhanced resilience and security for the digital space. It ensures strong protection for people and institutions to address rising threats with proactive approaches and new technological solutions. 

Applications that make use of the Internet of Things (IoT), such as the smart home (S-home), are growing in popularity as more and more smart gadgets are becoming available and affordable. However, existing authentication solutions may not be adequate for protecting IoT settings due to the peculiarities of these contexts, such as the utilization of devices with limited resources. This has led to the development of a variety of different authentication techniques that are specifically designed for the environment of the Internet of Things. An exhaustive overview of the current authentication techniques is given in this work. This chapter presents noteworthy contributions, which are outlined as follows: It begins by introducing a general model that was created using an S-Home use-case scenario. In order to identify potential entry points for an attack, it then conducts a threat assessment using the model as a basis. The study can be considered successful if it defines a workable set of security requirements for creating S-home authentication solutions. Third, based on the needs, a comparison of the current authentication methods is conducted, and recommendations are provided for achieving effective and efficient authentication in IoT settings. IoT computing provides additional benefits to users through the use of internet-connected smart appliances, objects, and gadgets. It is vital to process the data generated by intelligent IoT devices securely.

The infrastructure and technologies of computer security have undergone several improvements and modifications. There is a growing trend toward building an identity based on a combination of three factors: what you know, what you have, and who you are in the present (biometrics). Knowledge-based and token-based authentication systems have been deemed inadequate; however, new technology from the East has overcome these issues (biometrics). There is a chance that you will lose access to your resources if you forget your password. Biometrics is the science of identifying a person without disclosing private information. Biometrics is an authentication mechanism that uses a person's unique biological characteristics. This article examines the history and evolution of the many biometric identification modalities and the distinguishing characteristics that each of them possesses. The ability to recognize faces is one of the most fundamental ways in which humans have linked as individuals. Facial recognition is a sort of visual processing that works with human data. As there were no mirrors available to ancient humans, facial descriptions were typically established through the gaze of another person or, at best, through the description of the person's reflection in clear water. This was the normal practice for an extended period of time.

Connectivity, data proliferation, and technology have gained great advantages in this age of digitization. However, these advantages bring significant cybersecurity challenges. Especially with the advancement of malware, phishing attacks, and ransomware, advances like these are making it challenging to stay ahead in traditional security practices.

The advanced complexity in cyber security arises from these developments, like cloud computing, the Internet of Things, and mobile technologies. Organizations now have to shield not only their traditional networks but also environments based on the cloud, endpoints, and third-party integrations. The more interconnected devices and systems grow, the harder they are to secure from Distributed Denial of Service attacks and data breaches, for instance. An important challenge would be the time it takes to detect and respond to cyber incidents. Traditional security systems rely on static rules or signature-based methods, which make them ineffective at changing attack tactics. Artificial Intelligence has emerged as one of the significant transformative elements within the realm of cybersecurity, offering improved methodologies for the detection, prevention, and alleviation of cyber threats. With the complexity and intricacy of cyberattacks on the rise, AI-driven systems offer a much more advanced system than conventional security approaches. Further, artificial intelligence can predict potential weaknesses and automate redundant security functions so that cybersecurity experts can focus on strategic matters. This chapter analyses the increasing role of artificial intelligence in the cyber defense strategy and its potential use in different sectors of security.