Professor Celestine Iwendi

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item-item collaborative filtering. Presently, item recommendation is based completely on ratings like 1-5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users' reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with 79% accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of 21%, precision at 79%, recall at 80% and F1-Score at 79%. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

The WannaCry ransomware attack that happened in May 2017 represented a turning point for the modern cybersecurity landscape and, at the same time, spawned many lines of ethical debate related to discovering, using, and disclosing software vulnerabilities. This paper discusses ethical lessons from the WannaCry attack; it explores what this might mean for the respective roles and responsibilities of governments, technology companies, and cybersecurity professionals in managing zero-day vulnerabilities. It contemplates the broader implications for society as a whole of such decisions, and tensions between interests of national security and those of global cybersecurity. Ethical frameworks guiding future cybersecurity practices are proposed in the conclusion

Sentiment analysis is a subfield of natural language processing (NLP) that aims to determine the emotional tone and sentiment expressed in a given piece of text. It plays a crucial role in understanding the opinions, attitudes, and emotions of individuals towards various subjects, products, or events. With the rapid growth of online communication and social media, sentiment analysis has become increasingly important for businesses, governments, and researchers to gain valuable insights into public sentiment and make data-driven decisions. Deep Learning, a branch of machine learning, has shown remarkable success in various NLP tasks, including sentiment analysis. This research explores role of sentiment analysis in twitter data, application of deep learning techniques in sentiment analysis, focusing on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. In this paper sentiment analysis perform on a dataset of tweets related to the Pfizer vaccine. Here we have data related to vaccination tweets of 11021 users and sentiments of users. It processes text input, classifies feelings, and visualizes the findings using multiple natural language processing techniques and machine learning algorithms.

This paper presents a novel Intrusion Detection System (IDS) framework for securing Internet of Things (IoT) networks, leveraging advanced machine learning techniques. The proposed framework integrates Deep Neural Networks (DNNs) and Random Forest (RF) algorithms to enhance detection accuracy and robustness. Utilising the comprehensive CICIoT2023 dataset, the IDS model is rigorously trained and evaluated, demonstrating high efficacy in detecting and mitigating potential threats. However, the results also reveal shortcomings in detecting certain attack categories, such as command injection and SQL injection, indicating areas for further refinement. These findings contribute to the advancement of IoT security through the application of advanced machine learning techniques, while also highlighting the need for continued research to address identified shortcomings.

This paper presents a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for accurate age and gender classification from iris images. Using the GMBAMU-IRIS dataset, the model leverages CNNs for spatial feature extraction and RNNs for sequential pattern recognition. The proposed model achieved an age classification accuracy of 85.68% and a gender classification accuracy of 98.95%, outperforming traditional methods. These findings highlight the potential of hybrid models in enhancing biometric recognition systems. 1 Introduction Biometric recognition systems have emerged as critical components in various security and authentication applications, providing reliable and efficient means of identifying individuals based on their physiological and behavioral traits. Among the array of biometric modalities, the human iris stands out due to its unique patterns and remarkable stability throughout an individual's life. Iris recognition systems leverage the distinct textural patterns within the iris to achieve high levels of accuracy and reliability in personal identification and verification tasks [1][2]. Beyond the primary goal of identification, there is a growing interest in using iris images for soft biometric traits, such as age and gender classification. Accurate age and gender classification can significantly enhance the functionality of biometric systems by providing additional contextual information [3]. This additional layer of data can improve user experience , enable demographic-specific applications, and enhance the robust-ness of security systems by adding another factor of authentication [4].

The healthcare sector is expanding more quickly now that automation and digitization are progressing at a quicker rate. These advancements have given rise to creative models that create new opportunities for treatment delivery at reduced prices. The advent of the metaverse has attracted global interest as a cutting-edge digital environment with enormous potential to offer patients and medical professionals an extensive spectrum of health services. The potential of healthcare services to promote well-being and lifespan while simultaneously reducing the detrimental effects of sickness, injury, and illness highlights their important nature. Although the healthcare sector has developed swiftly, many issues still need to be resolved. These include the constant weight of lifelong chronic illnesses, growing healthcare costs, population aging, a lack of healthcare professionals, and a lack of resources. The metaverse might turn out to be an influential factor in this situation. It is the result of the integration of numerous cutting-edge technologies, including robotics, blockchain, quantum computing, virtual reality, artificial intelligence, augmented reality, and the Internet of Medical Devices. Using the potential of these cutting-edge technologies presents fascinating chances to investigate novel approaches for providing top-notch medical care and services. The metaverse offers fresh opportunities to enhance healthcare results because of its capacity to give patients and healthcare professionals lifelike experiences. This chapter provides a comprehensive examination of the primary healthcare enabling technologies, as well as the healthcare technological innovations specific to the metaverse. The necessity to bring healthcare services to people's living rooms has been prompted by these major challenges, which will be addressed in this chapter. This chapter will draw attention to the current literature, which addresses healthcare-related problems based on healthcare using the metaverse. The study offers prospective solutions as well as an identification of potential problems and obstacles when integrating the metaverse into healthcare, and future research directions are also discussed.

Maternal health has become an increasing health problem, especially in the Global South, where the risks, although reducing but not fast enough, continue to register high maternal mortality rates. This research work sought to provide an ergonomic and easily accessible solution by creating a machine learning web application that will be able to detect maternal health with an accuracy of 84%. The web application is a simple form where the user has a set list of questions and answers, based on those choices, a prediction is made. The research hopes this tool will be deployed in areas with high maternal mortality rates in order to bring down the avoidable risks of deaths experienced by pregnant women.

The main goal of anti-forensics tools and techniques are to " frustrate " not only the investigators but also the forensic tools used such as Sleuth Kit. Anti-forensics is quite exactly the opposite of Cyber Forensics. These tools affect an investigation negatively making it harder to reach a conclusion. Anti-forensic methods include operations such as deliberate deletion of data by means of overwriting it with new data by using anti-forensic tools, safely wiping out data that cannot be restored ever, altering the file properties to avoid being identified in timeline analysis and many other such methods. [1] While tools such as Autopsy, X-Ways, FTK, EnCase present the ability to detect some anti-forensic techniques if not all, these are not particularly dedicated for anti-forensic technique detection. To summarize, general forensic tools as mentioned above, perform several functions on the data source, of which anti-forensic is just one aspect. Though there exist tools like Timestomp Detector that are made for detecting altered file timestamps. Again, it is specific to only one feature and not many of the anti-forensic techniques. This dissertation aims to develop a dedicated framework that can help detect a few anti-forensic techniques based on user input. This will be integrated within a website format in order to make it easy for the users. This type of prototype could be very useful for investigators working on cases. Instead of going through the entire disk image, that could potentially take hours, investigators could separate any suspicious files and use this detection framework to identify if any of the files have been altered or managed using the anti-forensic techniques.

The GREAT (Games Realising Effective and Affective Transformation) project explores new approaches that foster climate change discussion and stimulate citizen reflection. However, some citizens have limited resources for participation, even though their engagement and contributions are crucial. To address this challenge, the authors present two studies that have deployed mini data sprints (MDS). The MDS approach uses interactive data applications and visualisations to provoke citizens? feelings, knowledge, and perspectives towards the climate conversation and presented data. These studies highlight how the MDS approach can provide data set recommendations, facilitate efficient and focused climate conversation, and improve the data literacy of the cohort.

The term blockchain was coined in 2008 by Satoshi Nakamoto. Initially, it was used for carrying out decentralised transactions to solve the problem of fake transactions. In the past few years, this was explored extensively for cryptocurrency only, but, over some time, its potential has been explored in many areas. The major reason for the growing interest in this particular technology is that it provides a secure, reliable, and trusted platform to perform digital activities. This is executed without the involvement of any third party.
Once the data is entered into the nodes, it is impossible to tamper it. Though blockchain is costly, it provides better solutions to many research problems in real- time. In recent times, researchers have explored blockchain in deep and used it in many applications such as building smart contracts, supply chain management, digital identity providers, voting systems, banking, and finance applications, P2P learning, and insurance sectors. Through this chapter, the readers will get a systematic and detailed study of blockchain in the insurance sector and smart contracts and its current applications in the insurance sector.
This chapter will also provide a fair idea of blockchain technology in the insurance sector and its usage in specific applications. In the end, a relevant set of further reading references will be provided.