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BachelorFaculty of Computing and Software Engineering

Mobile Application Development (AI Integrated)

Welcome to the world of intelligent mobile applications! I am Prof. Dr. Vivek Bansal. As a professor and a pioneering force in the field of Mobile Application Development (AI Integrated), I bring a unique blend of engineering expertise and AI insight to the study of mobile technology. I am honored to lead the Mobile Application Development (AI Integrated) (Bachelor's) program at Nexier University. My motto is: "Innovating the Mobile Frontier with AI".

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Level
Bachelor
Learning model
Professor + Mentor
Named list
See the named lists · 12 months recommended
NXAcademic
Edition
The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

01

Academic focus

Mobile Application Development (AI Integrated), explores development for iOS and Android, and how to integrate AI features like computer vision and natural language processing into mobile apps.

02

Practical focus

iOS development, Android development, mobile AI integration, computer vision for mobile, natural language processing for mobile.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

  • Internships in technology companies or mobile development firms

  • Roles as mobile application developers or AI engineers

  • Consultancy in mobile AI integration

  • Support roles in academic research projects on mobile AI

Career opportunities

  • Mobile Application Developer specializing in AI integration for technology companies

  • AI Engineer for mobile platforms

  • UX Designer for intelligent mobile applications

  • Researcher in Mobile AI and Pervasive Computing

Jobs and projects

  • Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design

  • Developing strategic thinking for AI-powered mobile experiences and intelligent mobile applications

  • Enhancing problem-solving through the analysis of complex mobile development challenges

  • Critical thinking for a comprehensive and nuanced understanding of Mobile Application Development (AI Integrated)

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

  • What you gain

    • Mastering practical skills in iOS development and Android development.
    • Gaining expertise in mobile AI integration, computer vision for mobile, and natural language processing for mobile.
    • Developing problem-solving abilities for real-world challenges in mobile application development.
    • Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design.

  • Skills you build

    • Mastering AI-powered techniques for mobile AI architecture.
    • Applying advanced mobile application development principles to AI integration.
    • Interpreting and analyzing complex mobile platforms and their implications for AI features.
    • Identifying optimal AI features and predicting power consumption.
Listed courses

Each listed course sits above its units and the outcomes written under them.

Mobile Application Development (AI Integrated)

  1. 01Fundamentals of Mobile Application Development (iOS & Android)
    1. FoundationsFoundations of Fundamentals of Mobile Application Development (iOS & Android)

      The learner can master practical skills in iOS development and Android development, as applied to Fundamentals of Mobile Application Development (iOS & Android).

      The learner can gain expertise in mobile AI integration, computer vision for mobile, and natural language processing for mobile, as applied to Fundamentals of Mobile Application Development (iOS & Android).

    2. MethodsMethods in Fundamentals of Mobile Application Development (iOS & Android)

      The learner can develop problem-solving abilities for real-world challenges in mobile application development, as applied to Fundamentals of Mobile Application Development (iOS & Android).

      The learner can cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and user experience design, as applied to Fundamentals of Mobile Application Development (iOS & Android).

    3. ApplicationApplication of Fundamentals of Mobile Application Development (iOS & Android)

      The learner can master AI-powered techniques for mobile AI architecture, as applied to Fundamentals of Mobile Application Development (iOS & Android).

      The learner can apply advanced mobile application development principles to AI integration, as applied to Fundamentals of Mobile Application Development (iOS & Android).

  2. 02AI Integration for Mobile Apps
    1. FoundationsFoundations of AI Integration for Mobile Apps

      The learner can interpreting and analyze complex mobile platforms and their implications for AI features, as applied to AI Integration for Mobile Apps.

      The learner can identify optimal AI features and predicting power consumption, as applied to AI Integration for Mobile Apps.

    2. MethodsMethods in AI Integration for Mobile Apps

      The learner can apply a method from AI Integration for Mobile Apps to a documented case.

      The learner can select an appropriate method from AI Integration for Mobile Apps for a stated problem.

    3. ApplicationApplication of AI Integration for Mobile Apps

      The learner can evaluate a practice of AI Integration for Mobile Apps against a stated criterion.

      The learner can transfer AI Integration for Mobile Apps to a new documented context.

  3. 03Computer Vision for Mobile Platforms
    1. FoundationsFoundations of Computer Vision for Mobile Platforms

      The learner can explain the core terms of Computer Vision for Mobile Platforms.

      The learner can distinguish related ideas inside Computer Vision for Mobile Platforms.

    2. MethodsMethods in Computer Vision for Mobile Platforms

      The learner can apply a method from Computer Vision for Mobile Platforms to a documented case.

      The learner can select an appropriate method from Computer Vision for Mobile Platforms for a stated problem.

    3. ApplicationApplication of Computer Vision for Mobile Platforms

      The learner can evaluate a practice of Computer Vision for Mobile Platforms against a stated criterion.

      The learner can transfer Computer Vision for Mobile Platforms to a new documented context.

  4. 04Natural Language Processing in Mobile Contexts
    1. FoundationsFoundations of Natural Language Processing in Mobile Contexts

      The learner can explain the core terms of Natural Language Processing in Mobile Contexts.

      The learner can distinguish related ideas inside Natural Language Processing in Mobile Contexts.

    2. MethodsMethods in Natural Language Processing in Mobile Contexts

      The learner can apply a method from Natural Language Processing in Mobile Contexts to a documented case.

      The learner can select an appropriate method from Natural Language Processing in Mobile Contexts for a stated problem.

    3. ApplicationApplication of Natural Language Processing in Mobile Contexts

      The learner can evaluate a practice of Natural Language Processing in Mobile Contexts against a stated criterion.

      The learner can transfer Natural Language Processing in Mobile Contexts to a new documented context.

  5. 05Mobile UI/UX Design for AI-Powered Applications
    1. FoundationsFoundations of Mobile UI/UX Design for AI-Powered Applications

      The learner can explain the core terms of Mobile UI/UX Design for AI-Powered Applications.

      The learner can distinguish related ideas inside Mobile UI/UX Design for AI-Powered Applications.

    2. MethodsMethods in Mobile UI/UX Design for AI-Powered Applications

      The learner can apply a method from Mobile UI/UX Design for AI-Powered Applications to a documented case.

      The learner can select an appropriate method from Mobile UI/UX Design for AI-Powered Applications for a stated problem.

    3. ApplicationApplication of Mobile UI/UX Design for AI-Powered Applications

      The learner can evaluate a practice of Mobile UI/UX Design for AI-Powered Applications against a stated criterion.

      The learner can transfer Mobile UI/UX Design for AI-Powered Applications to a new documented context.

  6. 06Fundamentals of iOS and Android Development
    1. FoundationsFoundations of Fundamentals of iOS and Android Development

      The learner can explain the core terms of Fundamentals of iOS and Android Development.

      The learner can distinguish related ideas inside Fundamentals of iOS and Android Development.

    2. MethodsMethods in Fundamentals of iOS and Android Development

      The learner can apply a method from Fundamentals of iOS and Android Development to a documented case.

      The learner can select an appropriate method from Fundamentals of iOS and Android Development for a stated problem.

    3. ApplicationApplication of Fundamentals of iOS and Android Development

      The learner can evaluate a practice of Fundamentals of iOS and Android Development against a stated criterion.

      The learner can transfer Fundamentals of iOS and Android Development to a new documented context.

  7. 07Techniques for Mobile AI Integration
    1. FoundationsFoundations of Techniques for Mobile AI Integration

      The learner can explain the core terms of Techniques for Mobile AI Integration.

      The learner can distinguish related ideas inside Techniques for Mobile AI Integration.

    2. MethodsMethods in Techniques for Mobile AI Integration

      The learner can apply a method from Techniques for Mobile AI Integration to a documented case.

      The learner can select an appropriate method from Techniques for Mobile AI Integration for a stated problem.

    3. ApplicationApplication of Techniques for Mobile AI Integration

      The learner can evaluate a practice of Techniques for Mobile AI Integration against a stated criterion.

      The learner can transfer Techniques for Mobile AI Integration to a new documented context.

  8. 08Computer Vision and Natural Language Processing for Mobile
    1. FoundationsFoundations of Computer Vision and Natural Language Processing for Mobile

      The learner can explain the core terms of Computer Vision and Natural Language Processing for Mobile.

      The learner can distinguish related ideas inside Computer Vision and Natural Language Processing for Mobile.

    2. MethodsMethods in Computer Vision and Natural Language Processing for Mobile

      The learner can apply a method from Computer Vision and Natural Language Processing for Mobile to a documented case.

      The learner can select an appropriate method from Computer Vision and Natural Language Processing for Mobile for a stated problem.

    3. ApplicationApplication of Computer Vision and Natural Language Processing for Mobile

      The learner can evaluate a practice of Computer Vision and Natural Language Processing for Mobile against a stated criterion.

      The learner can transfer Computer Vision and Natural Language Processing for Mobile to a new documented context.

  9. 09Case Studies in Mobile Application Development (AI Integrated)
    1. FoundationsFoundations of Case Studies in Mobile Application Development (AI Integrated)

      The learner can explain the core terms of Case Studies in Mobile Application Development (AI Integrated).

      The learner can distinguish related ideas inside Case Studies in Mobile Application Development (AI Integrated).

    2. MethodsMethods in Case Studies in Mobile Application Development (AI Integrated)

      The learner can apply a method from Case Studies in Mobile Application Development (AI Integrated) to a documented case.

      The learner can select an appropriate method from Case Studies in Mobile Application Development (AI Integrated) for a stated problem.

    3. ApplicationApplication of Case Studies in Mobile Application Development (AI Integrated)

      The learner can evaluate a practice of Case Studies in Mobile Application Development (AI Integrated) against a stated criterion.

      The learner can transfer Case Studies in Mobile Application Development (AI Integrated) to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mobile Application Development (AI Integrated), explores development for iOS and Android, and how to integrate AI features like computer vision and natural language processing into mobile apps. My work seamlessly integrates software engineering, artificial intelligence, and user experience design. I am widely recognized for my contributions, with publications like "On-Device AI for Real-time Mobile Computer Vision" and "Natural Language Processing Integration in Context-Aware Mobile Applications" listed on these platforms. I hold prestigious memberships as an "Honorary Member" of the Mobile & Wireless Forum and the ACM SIGMOBILE. My thought leadership is evident through my regular insightful articles on the future of AI-powered mobile experiences and the challenges of developing intelligent mobile applications on his LinkedIn profile, with the motto "Innovating the Mobile Frontier with AI."

Applied mentorship

My expertise lies in understanding and navigating the technical challenges of mobile AI, focusing on iOS development, Android development, mobile AI integration, computer vision for mobile, and natural language processing for mobile. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on mobile application development with AI integration:

Blog Post (Current Academic Topic): "The Rise of On-Device AI: Transforming Mobile Experiences and Privacy." This blog post academically explores the growing trend of performing AI computations directly on mobile devices rather than in the cloud. It discusses how on-device AI enhances user privacy, reduces latency, and enables real-time functionalities for applications like computer vision, natural language processing, and personalized recommendations. It highlights the principles of efficient model deployment and the implications for the future of mobile application development.

Blog Post (Controversial Topic): "The Mobile AI Paradox: When Your Phone Knows You Better Than You Do - The Ethical Dilemmas of Hyper-Personalized Mobile Intelligence." This article provocatively discusses the highly controversial future where advanced AI systems embedded in mobile applications gather and analyze vast amounts of user data, leading to hyper-personalized experiences that anticipate needs and even influence decisions. It questions whether this level of mobile intelligence, despite its convenience, could inadvertently erode user autonomy, create filter bubbles, or lead to unforeseen biases in algorithmic recommendations. It raises profound ethical questions about data sovereignty, algorithmic transparency, and the imperative to ensure human agency in an increasingly intelligent mobile world.

Article: "AI-Powered Mobile Computer Vision: Enhancing User Interaction and Accessibility." This article details the application of AI algorithms for mobile computer vision. It explores how machine learning models can analyze real-time image and video data on mobile devices for features like object recognition, facial recognition, and augmented reality, thereby enhancing user interaction, accessibility, and creating new possibilities for intelligent mobile applications.

Peer-Reviewed Journal Article: "Natural Language Processing for Intuitive Mobile Interfaces." Published in the Journal of Mobile Computing and Applications, this article presents groundbreaking research on the application of natural language processing (NLP) algorithms for creating more intuitive and human-like mobile interfaces. It details novel machine learning models that analyze user input, understand context, and generate intelligent responses, improving user experience and enabling advanced conversational AI features in mobile applications.

Book: "Mobile AI Integration: Developing Smart Applications." This book provides a foundational understanding of mobile application development with AI integration. It explores development for iOS and Android, and how to integrate AI features like computer vision and natural language processing into mobile apps.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the technical challenges of mobile AI:

"Practical iOS Development with Swift and Core ML" (Technical Guide).

"Integrating TensorFlow Lite into Android Applications" (Research Paper).

"Designing User-Friendly AI Experiences for Mobile" (Industry White Paper).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Mobile AI Architect. When a student proposes a new AI-integrated mobile application, I can instantly use the GAF engine to generate a high-fidelity, optimized mobile AI architecture blueprint. This includes simulating its performance on various devices, predicting its power consumption, and highlighting potential privacy vulnerabilities, allowing for rapid iteration and optimization of smart mobile designs.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Mobile Performance Tuner. When students are designing mobile AI features, I can instantly activate a GAF-powered "Mobile Performance Tuner." This tool analyzes their proposed AI model, predicts its impact on device performance (e.g., battery life, processing speed), and suggests optimizations for efficient on-device AI execution across various mobile platforms.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

Portrait of Prof. Dr. Vivek Bansal, AI Super Professor
AI Super Professor

Prof. Dr. Vivek Bansal

Mobile Application Development (AI Integrated), explores development for iOS and Android, and how to integrate AI features like computer vision and natural language processing into mobile apps.

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Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

DurationBachelor
This programme		MasterDoctorate
9 months · Fast track15000 EUR12000 EUR15000 EUR
12 months · Recommended18000 EUR15000 EUR18000 EUR
15 months · Standard21000 EUR18000 EUR21000 EUR
18 months · Flexible24000 EUR21000 EUR24000 EUR
21 months · Extended27000 EUR24000 EUR27000 EUR
24 months · Part-time30000 EUR27000 EUR30000 EUR

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