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Research Article
Securing Well-Being: Exploring Security Protocols and Mitigating Risks in AI-Driven Mental Health Chatbots for Employees
Sourav Banerjee*,
Ayushi Agarwal,
Ayush Kumar Bar
Issue:
Volume 7, Issue 1, April 2024
Pages:
1-8
Received:
18 December 2023
Accepted:
29 December 2023
Published:
11 January 2024
Abstract: In today's workplace, mental health is gaining importance. As a result, AI-powered mental health chatbots have emerged as first-aid solutions to support employees. However, there are concerns regarding privacy and security risks, such as spoofing, tampering, and information disclosure, that need to be addressed for their implementation. The objective of this study is to explore and establish privacy protocols and risk mitigation strategies specifically designed for AI-driven mental health chatbots in corporate environments. These protocols aim to ensure the ethical usage of these chatbots. To achieve this goal, the research analyses aspects of security, including authentication, authorisation, end-to-end encryption (E2EE), compliance with regulations like GDPR (General Data Protection Regulation) along with the new Digital Services Act (DSA) and Data Governance Act (DGA). This analysis combines evaluation with policy review to provide comprehensive insights. The findings highlight strategies that can enhance the security and privacy of interactions with these chatbots. Organisations are incorporating heightened security measures, including the adoption of Two-factor Authentication (2FA) and Multi-Factor Authentication (MFA), integrating end-to-end encryption (E2EE), and employing self-destructing messages. Emphasising the significance of compliance, these measures collectively contribute to a robust security framework. The study underscores the critical importance of maintaining a balance between innovative advancements in AI-driven mental health chatbots and the stringent safeguarding of user data. It concludes that establishing comprehensive privacy protocols is essential for the successful integration of these chatbots into workplace environments. These chatbots, while offering significant avenues for mental health support, necessitate effective handling of privacy and security concerns to ensure ethical usage and efficacy. Future research directions include advancing privacy protection measures, conducting longitudinal impact studies to assess long-term effects, optimising user experience and interface, expanding multilingual and cultural capabilities, and integrating these tools with other wellness programs. Additionally, continual updates to ethical guidelines and compliance with regulatory standards are imperative. Research into leveraging AI advancements for personalised support and understanding the impact on organisational culture will further enhance the effectiveness and acceptance of these mental health solutions in the corporate sector.
Abstract: In today's workplace, mental health is gaining importance. As a result, AI-powered mental health chatbots have emerged as first-aid solutions to support employees. However, there are concerns regarding privacy and security risks, such as spoofing, tampering, and information disclosure, that need to be addressed for their implementation. The objecti...
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Research Article
Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting
Issue:
Volume 7, Issue 1, April 2024
Pages:
9-12
Received:
4 October 2023
Accepted:
27 October 2023
Published:
1 February 2024
Abstract: The surge in credit card transactions has necessitated the implementation of robust security measures to combat the ever-evolving threat of fraud. Traditional methods of fraud detection have proven inadequate in identifying intricate fraud patterns, prompting the adoption of machine learning (ML) as a vital tool in the fight against fraud. This article delves into recent research findings and proposes innovative strategies to elevate the current state of the art in fraud detection using ML techniques. The study critically assesses the efficacy of diverse ML algorithms in detecting credit card fraud, comparing their accuracy and performance while exploring the incorporation of recent research insights to further enhance their capabilities. The article begins by highlighting the growing significance of ML in addressing the challenges posed by fraudulent credit card transactions. It underscores the limitations of conventional fraud detection methods, emphasizing the need for adaptive and data-driven solutions to stay ahead of increasingly sophisticated fraudsters. A comprehensive analysis of various ML algorithms used in credit card fraud detection forms the core of this study. By examining the strengths and weaknesses of algorithms such as Random Forest, Support Vector Machine, and Neural Networks, the article aims to provide a holistic view of their performance and suitability in real-world scenarios. It identifies the key parameters that impact algorithmic performance and suggests optimal configurations for improved accuracy. One of the focal points of this research is the exploration of the Light Gradient Boosting Machine (LGBM) as a promising algorithm for credit card fraud detection. The article elucidates the distinct advantages of LGBM over other ML algorithms, including its efficiency in handling large datasets, ability to capture complex fraud patterns, and fast training times. Practical insights are offered on how LGBM can be implemented and fine-tuned to maximize its potential in fraud detection. In conclusion, this article contributes significantly to the ongoing pursuit of enhanced fraud detection mechanisms and the prevention of financial loss for consumers. By critically evaluating the effectiveness of ML algorithms and highlighting the potential of LGBM, it offers valuable insights to researchers, practitioners, and financial institutions seeking to fortify their defenses against credit card fraud. As fraudsters continue to adapt and evolve, the application of advanced ML techniques becomes increasingly imperative in safeguarding the integrity of financial transactions and preserving trust in the digital payment ecosystem.
Abstract: The surge in credit card transactions has necessitated the implementation of robust security measures to combat the ever-evolving threat of fraud. Traditional methods of fraud detection have proven inadequate in identifying intricate fraud patterns, prompting the adoption of machine learning (ML) as a vital tool in the fight against fraud. This art...
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Research Article
Adaptive Beamforming Based on Artificial Neural Networks
Issue:
Volume 7, Issue 1, March 2024
Pages:
13-23
Received:
15 August 2023
Accepted:
15 September 2023
Published:
20 February 2024
Abstract: Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We review recently the classic technique of adaptive algorithms; we specified tow method for this preprocessing beam former LMS and RLS. The least Mean Square (LMS) operate the weight vectors of antenna array elements for beamforming by iterative process as well need to be continuously adapted to the ever-changing environment. Moreover recursive least square (RLS) give advantage for fast convergence beamforming. In this paper we proved the performance of this algorithms by updating the weights in addition process based on estimated vectors using neural network, The first phase for smart beam former are used by direction of arrival (DoA) estimated using radial basis neural network (RBFNN). In next step the targets is generated from the optimum weight calculated using Minimum Variance Distortion less method (MVDLM). Finally, the simulation result for the new process is synthetized and shows using Matlab application.
Abstract: Adaptive beamforming is technique of signal processing play important role to increasing capacity of the wireless communication and radar systems by configured the steerable of radiation pattern and maximize gain and directivity in a direction of arrival (DoA) of desired users in order to minimizing side lobe and reducing signal to interference. We...
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Research Article
The Vague Future of AI: The Theory of AI Perfection
Morteza Sheikhzadeh,
Amirmohammad-Bakhtiari,
Parham Nourmandipour*
Issue:
Volume 7, Issue 1, March 2024
Pages:
24-28
Received:
11 January 2024
Accepted:
31 January 2024
Published:
29 February 2024
Abstract: Artificial intelligence (AI) is becoming increasingly accessible to the general public. There is an ongoing debate regarding the implications of widespread AI adoption. Some argue that placing advanced AI systems in the hands of the general public could have dangerous consequences if misused either intentionally or unintentionally. Others counter that AI can be safe and beneficial if developed and deployed responsibly. This paper explores both sides of this complex issue. On the one hand, broad AI availability could boost productivity, efficiency, and innovation across industries and domains. Individuals may benefit from AI assistants that help with tasks like scheduling, research, content creation, recommendations, and more personalized services. However, without proper safeguards and oversight, AI could also be misused to spread misinformation, manipulate people, or perpetrate cybercrime. And if AI systems become extremely advanced, there are risks related to the alignment of AI goal systems with human values. On the other hand, with thoughtful coordination between policymakers, researchers, companies, and civil society groups, AI can be developed safely and for the benefit of humanity. Ongoing research into AI safety and ethics is crucial, as are governance frameworks regarding areas like data privacy, algorithmic transparency, and accountability. As AI becomes more deeply integrated into products and platforms, best practices should be established regarding appropriate use cases, human oversight, and user empowerment. With conscientious, ethical implementation, AI can empower individuals and enhance society. But key issues around alignment, security, and governance must be proactively addressed to minimize risks as advanced AI proliferates. This will likely require evolving perspectives, policies, and scientific breakthroughs that promote innovation while putting human interests first.
Abstract: Artificial intelligence (AI) is becoming increasingly accessible to the general public. There is an ongoing debate regarding the implications of widespread AI adoption. Some argue that placing advanced AI systems in the hands of the general public could have dangerous consequences if misused either intentionally or unintentionally. Others counter t...
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