Computational Approaches to Diabetes Risk Assessment: A Review of Data-Driven Techniques Agrimaa Singh Thakur, Amit Verma Journal of Behavioral Data Science, 2026 Over 540 million people worldwide suffer from diabetes mellitus, making it a serious global health concern. The advancement of robust predictive models that surpass traditional risk assessment approaches has demonstrated significant potential due to machine learning techniques. This thorough analysis summarizes the state of the art in machine learning-based diabetes prediction systems by examining algorithmic approaches, dataset properties, and performance indicators. The analysis shows how advanced ensemble and deep learning techniques have replaced more conventional statistical methods in order to achieve better results. Critical drawbacks still exist, nonetheless, such as an excessive dependence on datasets with a restricted demographic, a lack of real-world validation, and inadequate model interpretability for clinical acceptability. Regulatory obstacles, population-specific dataset variability, and discrepancies between algorithmic performance and therapeutic impact are some of the main obstacles. In order to convert advancements into clinically useful systems, future priorities include creating representative datasets, putting explainable artificial intelligence (AI) into practice, and carrying out prospective clinical studies.
A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities Surjeet Dalal, Neeraj Dahiya, Amit Verma, Neetu Faujdar, Sarita Rathee, Vivek Jaglan, Uma Rani, Dac-Nhuong Le Recent Advances in Computer Science and Communications, 2026 Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.
GAN-CSA: Enhanced Generative Adversarial Networks for Accurate Detection and Surgical Guidance in Skull Base Brain Metastases Surjeet Dalal, Neeraj Dahiya, Shakti Kundu, Amit Verma, Gaytri Devi, Manel Ayadi, Mitiku Dubale, Arshad Hashmi International Journal of Computational Intelligence Systems, 2025 Skull-base brain metastases pose significant diagnostic and surgical challenges due to their proximity to vital neurological structures. We propose an enhanced Generative Adversarial Network (GAN) model optimised with the Crow Search Algorithm (CSA) to improve detection accuracy and intraoperative decision-making. The GAN framework facilitates high-fidelity image generation and segmentation, while CSA fine-tunes hyperparameters for improved model stability and accuracy. Trained on high-resolution brain MRI datasets with expert annotations, our model achieved a precision of 97.43%, surpassing existing approaches in accuracy and robustness. The system accurately delineates tumour margins and adjacent anatomical structures in real-time, enhancing surgical guidance and reducing operative risks. The inclusion of CSA significantly improved GAN convergence and reduced false positives. This integrated GAN-CSA approach shows promise for revolutionizing neuro-oncology practices by enabling safer and more precise skull base surgeries. As an initial proof-of-concept, the evaluation was limited to 156 MR volumes from a single scanner, and future cross-centre studies will be pursued to establish robustness across varying field strengths, coils, and imaging protocols.
IoT and AIoT: Applications, challenges and optimization Amit Verma, Raman Kumar Future of Computing Ubiquitous Applications and Technologies, 2024 The Internet of Things (IoT) has rapidly gained popularity as a technology that enables devices to communicate with each other and the Internet, opening up a world of possibilities for new applications and services. This chapter provides an overview of IoT, its applications, and the challenges that need to be addressed in its deployment. IoT and AIoT are two of the most significant technological innovations of the 21st century. IoT allows physical devices to connect and exchange data, while AIoT enables these devices to learn, analyze, and make decisions based on the data they collect. The term “AIOT” stands for “Artificial Intelligence of Things.” AIOT refers to the integration of Artificial Intelligence (AI) technologies with the Internet of Things (IoT) ecosystem. In essence, AIOT combines the capabilities of AI and IoT to create intelligent, self-learning systems that can analyze, interpret, and respond to data generated by IoT devices. Together, these technologies offer numerous benefits such as increased efficiency, better decision-making capabilities, and improved outcomes across industries like healthcare, manufacturing, transportation, and agriculture. As more devices and systems become connected, IoT and AIoT will continue to play a critical role in shaping the future of our world. IoT and AIoT have the potential to transform the way we live and work. By enabling devices to communicate and share data, IoT can help us create more efficient and effective systems, and by integrating AI technologies, IoT devices can become smarter and more autonomous. This means that devices can analyze data in real-time, make decisions, and adapt to changing conditions without human intervention. For example, in smart cities, IoT and AIoT can help reduce traffic congestion by optimizing traffic flows, and in healthcare, they can help monitor patients remotely and alert healthcare providers when necessary. As more devices and systems become connected, we can expect IoT and AIoT to become increasingly sophisticated, offering new opportunities for innovation and growth in various industries. However, as with any new technology, there are also potential risks and challenges that must be addressed, such as security and privacy concerns, and the need for new regulations and standards to ensure the safe and ethical use of these technologies.
