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All Journal Communication in Biomathematical Sciences Journal Of World Science International Journal of Multidisciplinary Sciences and Arts Enrichment: Journal of Multidisciplinary Research and Development BULLET : Jurnal Multidisiplin Ilmu
Zeb, Shah
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Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Social Sciences

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Ai-Driven Robotics and Automation: The Evolution of Human-Machine Collaboration Lodhi, Shahrukh Khan; Zeb, Shah
Journal of World Science Vol. 4 No. 4 (2025): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v4i4.1389

Abstract

AI-driven robotics has transformed industries through enhanced automation, yet challenges like ethical dilemmas, workforce displacement, and cybersecurity gaps persist. While prior research focused on functional applications, emotional intelligence and bio-inspired designs remain underexplored. This study examines the integration of emotionally intelligent and bio-inspired robots into human-machine collaboration, evaluates ethical governance frameworks, and proposes solutions for global regulatory harmonization. A mixed-method approach was employed, combining systematic literature reviews of 72 peer-reviewed articles (2014–2024) and case studies of AI robotics in healthcare, manufacturing, and agriculture. Data were analyzed via thematic coding and SWOT analysis. Key innovations include socially intelligent robots for elderly care, BCIs for neural-controlled prosthetics, and swarm robotics for precision agriculture. Ethical challenges like bias in hiring algorithms and accountability gaps in autonomous systems were identified, necessitating transparent AI audits. The research advocates for adaptive regulatory models to balance innovation with ethical safeguards, emphasizing human-centric collaboration. It calls for international standards to address bias, cybersecurity, and liability, offering a roadmap for policymakers and industries to harness AI robotics responsibly.
AI for Predictive Maintenance: Reducing Downtime and Enhancing Efficiency Zeb, Shah; Lodhi, Shahrukh Khan
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 1 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i1.338

Abstract

The implementation of AI predictive maintenance technology by organizations results in operational alterations by providing predictive equipment data instead of traditional maintenance protocols. Artificial intelligence with machine learning technology along with IoT sensors brings organizations two distinct advantages including improved equipment prediction performance and better operations and budget management which reduces unexpected production breakdowns. Better operational performance and longer equipment durability accompany improved safety practices which the manufacturing industry alongside transportation healthcare sectors and aerospace and energy operations have noticed. The implementation of AI-based predictive maintenance meets various deployment challenges caused by initial cost expenses and contradictory data quality as well as security threats during integration of new infrastructure with existing platforms. Edge computing technology provides platforms that link digital duplicates with 5G capabilities to generate autonomous AI repair protocols. The implementation of artificial intelligence-based medical maintenance will progress from specialized practice to fundamental core industrial operations since it enhances equipment stability while decreasing operational breakdowns to achieve superior industrial outcomes in every sector.
The Role of AI in Circular Manufacturing: Towards a Zero-Waste Economy Provides its Headings Lodhi, Shahrukh Khan; Zeb, Shah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 1 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i1.339

Abstract

The transition to a zero-waste economy necessitates innovative approaches to circular manufacturing, where Artificial Intelligence (AI) plays a pivotal role. This study examines how AI technologies—including predictive maintenance, machine learning, and blockchain—enhance resource efficiency, reduce waste, and optimize supply chains in circular manufacturing systems. Employing a qualitative methodology, the research synthesizes literature from peer-reviewed journals and industrial case studies to analyze AI's applications across product design, production, and end-of-life processing. Findings reveal that AI-driven solutions significantly improve material recovery, operational transparency, and demand forecasting, yet face hurdles such as high costs, data quality issues, and energy demands. The study proposes policy-industry collaboration and advanced technologies like digital twins to overcome these barriers. Implications suggest that AI integration not only accelerates sustainability goals but also fosters economic resilience, as evidenced by reduced emissions and extended product lifecycles. This research contributes a framework for scalable, AI-enabled circular manufacturing, addressing gaps in existing literature while highlighting future directions for innovation in sustainable industrial practices.
A Systematic Review of Health Informatics Applications in Clinical Practice and Healthcare Management Javeedullah, Mohammed; Zeb, Shah
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 04 (2022): BULLET : Jurnal Multidisiplin Ilmu ( Agustus-September)
Publisher : CV. Multi Kreasi Media

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Abstract

Healthcare is improved in both healthcare delivery and administrative =tasks by integrating technology and data management through health informatics. This analysis looks at how some health informatics systems are used, for example, Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), Health Information Exchanges (HIE) and telemedicine platforms. As a result of these technologies, patient safety has improved, quality of care has risen and daily work is more efficient due to updated data, streamlined procedures and evidence being easily used. Issues related to making different systems work together, safeguarding private information and being reluctant to change keep many from using AI widely. It also looks at new technologies like artificial intelligence, block chain and telehealth which could make healthcare better. Overcoming these issues and using these new methods gives health informatics the ability to make healthcare more efficient, personalized and sustainable worldwide.
Healing with Intelligence: A Review of AI-Enabled Healthcare Solutions Zeb, Shah
International Journal of Multidisciplinary Sciences and Arts Vol. 4 No. 3 (2025): International Journal of Multidisciplinary Sciences and Arts, Article July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v4i3.6839

Abstract

Artificial intelligence (AI) is taking the healthcare field by storm as healthcare providers adopt its use to inform data-based decisions, improve clinical decision-making, and make their operations more efficient. This review discusses the fundamentals of AI, including machine learning, deep learning, and natural language processing technologies and how they can be applied to diagnostics, individualized treatment, remote patient monitoring, hospital operations, and population health monitoring. The strengths of AI are the ability to identify early disease, custom care plans, and precognitive analysis to direct resources. Nevertheless, integration in healthcare systems is stalled by risk of having biased algorithms, data privacy, interoperability, and changing demands of regulatory guidelines. A solution to such barriers is interdisciplinary: combining multiple views to develop and validate the models legitimately, with transparency and trustworthiness. Future trends, such as explainable AI, federated learning and integration of the robots aim at a more flexible and patient-centered future. After all, the best role that AI can play is to augment human expertise by providing more precise, proactive and fair care but without losing that critical human touch in healthcare.
A Nonlinear Delay Mathematical Model for Predicting Chlamydia Dynamics and Intervention Effects Zeb, Shah; Mohd Yatim, Siti Ainor; Kamran, Ayesha; Zulfiqar, Sabahat; Rafiq, Muhammad; Mohamad Noor, Nursyazwani
Communication in Biomathematical Sciences Vol. 8 No. 2 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.2.1

Abstract

Chlamydia is a widespread sexually transmitted infection in Europe, often leading to complications such as rectal discomfort, throat inflammation, and reactive arthritis. This study presents a novel nonlinear delay differential equation model that enhances the classical SEIAISR framework to more accurately represent Chlamydia transmission dynamics. The model integrates biologically justified exponential time delays to reflect incubation periods and the delayed impact of interventions like condom use, routine screening, partner reduction, and microbiome health. We establish the existence and uniqueness of solutions using the Banach fixed point theorem and analyze the model’s dynamics by computing the basic reproduction number and studying equilibria and their stability via Lyapunov functions and Routh-Hurwitz criteria. A sensitivity analysis identifies key epidemiological drivers. For numerical simulation, we employ Euler’s method, the Runge-Kutta 4th order (RK4) method, and a specially developed non-standard finite difference (NSFD) scheme. The NSFD approach preserves critical properties such as positivity and stability, making it suitable for realistic long-term predictions. Results highlight the importance of timely interventions and show the superiority of structurepreserving numerical methods. The findings support the development of more targeted and effective strategies to reduce chlamydia transmission and complications among high-risk groups, reinforcing evidence-based decisionmaking within the healthcare system.
Co-Authors Javeedullah, Mohammed Kamran, Ayesha Lodhi, Shahrukh Khan Mohamad Noor, Nursyazwani Mohd Yatim, Siti Ainor Muhammad Rafiq Zulfiqar, Sabahat