International Journal of Human Computer Interaction and Data Mining

Blockchain Based KYC System

Sahil Sagvekar — 2024-05-09

The financial industry and various other sectors have witnessed a significant transformation with the advent of blockchain technology. This research paper presents a comprehensive study and implementation of a blockchain-based KYC (Know Your Customer) system aimed at revolutionising identity verification processes. KYC is a fundamental aspect of customer onboarding, compliance, and security in various industries, including banking, finance, and e-commerce. However, traditional KYC methods suffer from inefficiencies, security vulnerabilities, and the risk of data breaches. In this paper, we propose a novel approach that leverages the immutable and decentralised nature of blockchain technology to enhance the KYC process. Our blockchain-based KYC system addresses key challenges such as data privacy, security, and interoperability across institutions. We implement a permissioned blockchain network to ensure data integrity, immutability, and accessibility while adhering to regulatory requirements.

Study of NLP Models and Their Efficacy in Detection of Early Signs of Depression and Suicidal Tendencies

Atman Shastri — 2024-05-09

This research examines the potential of Natural Language Processing (NLP) techniques, particularly BERT, GPT-3, and XLNet, to identify early signs of depression and suicidal tendencies by analysing written text. The study investigates the ability of these NLP models to detect linguistic patterns associated with mental health issues. The main concept is to look for language patterns in written text—such as blog posts, online conversations, or even creative writing—that are connected to mental health issues. Consider a social media site that uses natural language processing (NLP) to identify posts that may be troubling and discreetly link users to mental health resources. These models could be used by online treatment platforms to identify people who want urgent assistance and to personalise sessions. The paper acknowledges the difficulties as well; the possibility of bias in the training set of NLP models is one thing to be concerned about. Furthermore, written language may be imprecise, leading to misunderstandings of sarcasm or metaphorical speaking. The research suggests using fundamental NLP models to address this and give a more in-depth comprehension of the text’s emotional undertones. This study expands on the foundation of sentiment analysis by utilising the capabilities of the BERT, GPT-3, and XLNet models. These sophisticated NLP techniques are capable of capturing the complex language patterns linked to suicidal thoughts and depression. We go beyond general sentiment analysis to spot minute changes in language usage, such as an increase in words associated with negative emotions or a decrease in language complexity. The goal of this research is to use text analysis from multiple sources to create an early warning system for mental health emergencies. The possible impact is in providing timely help and intervention to individuals in need, which could eventually result in lifesaving. We use precision, recall, and F1-score to statistically evaluate the effectiveness of XLNet, GPT-3, and BERT in text-based depression identification.

Decrypting the Dark Web: Challenges, Solutions and Future of Combating Cybercrime

Vivek Shukla — 2024-05-09

The realm of the dark web presents a complex landscape where illicit online economies thrive, presenting significant challenges for law enforcement agencies worldwide. This paper delves into the intricate interplay between the dark web and cybercrime, examining existing methodologies, their inherent limitations, and positing innovative strategies involving technology, policy, and collaboration to bolster future efforts in combating these nefarious activities.
At the core of the dark web’s functionality lies its facilitation of various illicit transactions spanning narcotics, weaponry, stolen data, counterfeit merchandise, and clandestine hacking services. The anonymity it affords users complicates traditional investigative approaches, necessitating novel tactics to pierce the veil of secrecy. Crucially, addressing jurisdictional ambiguities and harnessing encryption technologies emerge as pivotal endeavors in unmasking perpetrators and dismantling criminal networks operating within these digital shadows.
In navigating the intricate web of anonymity, international cooperation emerges as an indispensable pillar in the fight against cybercrime. Efforts to forge robust partnerships between law enforcement agencies, academic institutions, and the technology sector are imperative for fostering a collaborative ecosystem capable of swiftly responding to evolving threats. By pooling resources, sharing intelligence, and leveraging specialised expertise, stakeholders can enhance their collective capacity to detect, investigate, and prosecute cybercriminal activities across borders.
Moreover, the cultivation of a comprehensive regulatory framework is essential to mitigating the proliferation of illicit activities on the dark web. Strategic interventions, such as targeted legislation and policy initiatives, serve to deter malicious actors while safeguarding the integrity of digital ecosystems. Concurrently, fostering a culture of responsible digital citizenship through education and awareness initiatives is instrumental in fostering a resilient defense against cyber threats.

Singularity of the Future: Exploring the Impact of Advanced Artificial Intelligence

Sarvesh Sambhaji Bhapkar — 2024-05-09

This research delves deep into the concept of singularity, a profound framework for envisioning the future characterised by the inevitable and irreversible advancement of technology, fundamentally altering the course of human civilization. Central to this exploration is an in-depth analysis of the current state of artificial intelligence (AI), elucidating its capabilities, limitations, and trajectory. Moreover, the study investigates potential pathways forward, considering various scenarios and their implications for society. Beyond technological advancements, the research meticulously examines the ethical, social, and economic dimensions of approaching this pivotal juncture in technological evolution. By critically evaluating these multifaceted aspects, the study aims to provide valuable insights into navigating the complexities of a future deeply intertwined with advanced AI technologies. Through comprehensive examination and thoughtful analysis, the research contributes to a deeper understanding of the challenges and opportunities that accompany the journey towards singularity, facilitating informed decision-making and strategic planning for individuals, organisations, and policymakers alike.

Addressing Bias and Fairness in Natural Language Processing

Ayesha Shaikh — 2024-06-11

Natural Language Processing (NLP) technologies have made remarkable advancements in recent years, transforming the way we interact with and understand textual data. However, these advancements have also brought to light a critical issue: the presence of bias in NLP models and the potential for unfair or discriminatory outcomes. This research paper delves into the pressing concerns surrounding bias and fairness in NLP and proposes novel approaches to mitigate these challenges. The paper begins by discussing the sources of bias in NLP, including biased training data, skewed representation, and societal prejudices embedded in language. It highlights real-world examples of NLP systems producing biased or unfair results, underscoring the urgency of addressing this issue. The study emphasizes the significance of evaluating fairness not only in terms of demographic parity but also with respect to the consequences of model predictions for different subpopulations. It presents case studies of practical implementations, including fair automated hiring and unbiased language translation.
In conclusion, this research paper calls for an ethical, transparent, and community-driven approach to address bias and fairness in NLP. It underscores the necessity of continuous research and development in this domain to ensure that NLP technologies are not only cutting-edge but also uphold the principles of fairness and equity.