Research publications

Sharoon Saleem, Fawad Hussain, Naveed Khan Baloch

This research proposes a hybrid whale-optimization and genetic approach for mapping real-time applications onto 2D NoC, showing improvements in power, energy, latency, and convergence behavior.

Waqar Amin, Fawad Hussain, Sheraz Anjum, Sharoon Saleem, Naveed Khan Baloch, Yousaf Bin Zikria, Heejung Yu

This paper presents a grey-wolf-optimization-based application mapping approach for network-on-chip systems, combining clustering and polynomial-regression-assisted heuristics to reduce communication cost, energy use, and computation time.

Muhammad Muzamal Shahzad, Zubair Saeed, Asima Akhtar, Hammad Munawar, Muhammad Haroon Yousaf, Naveed Khan Baloch, Fawad Hussain

This review surveys the foundations, behaviors, algorithms, applications, and future directions of swarm robotics, highlighting the gap between simulation-driven research and real-world hardware deployment.

Misha Urooj Khan, Sana Samer, Hareem Khan, Naveed Khan Baloch, Fawad Hussain, Sung Won Kim, Yousaf Bin Zikria, Mohammad Dahman Alshehri

This paper presents an artificial-neural-network-based approach for cardiovascular disease prediction from phonocardiogram signals using selected spectral features, targeting accurate and low-cost clinical screening.

Farrukh Mehmood, Naveed Khan Baloch, Fawad Hussain, Waqar Amin, M. Shamim Hossain, Yousaf Bin Zikria, Heejung Yu

This paper proposes an Andean condor algorithm for application mapping in network-on-chip systems, aiming to reduce communication cost and improve energy-efficient placement for real-time workloads.

Muhammad Junaid Mohiz, Naveed Khan Baloch, Fawad Hussain, Sharoon Saleem, Yousaf Bin Zikria, Heejung Yu

This paper proposes a cuckoo-search optimization approach with Levy flight for task mapping in NoC-based systems, improving communication cost, energy consumption, and average packet latency through a hybrid greedy and metaheuristic placement strategy.

Misbah Farooq, Fawad Hussain, Naveed Khan Baloch, Fawad Riasat Raja, Heejung Yu, Yousaf Bin Zikria

This paper studies how feature-selection methods affect speech emotion recognition using a deep convolutional neural network, showing that careful acoustic feature selection can improve recognition accuracy and computational efficiency.

Muhammad Rashid, Naveed Khan Baloch, Muhammad Akmal Shafique, Fawad Hussain, Shahroon Saleem, Yousaf Bin Zikria, Heejung Yu

This paper presents a fault-tolerant router architecture for network-on-chip systems in heterogeneous computing environments, focusing on permanent-fault tolerance to improve reliability and maintain performance in IoT-oriented architectures.

Shamila Akhtar, Fawad Hussain, Fawad Riasat Raja, Muhammad Ehatisham-ul-haq, Naveed Khan Baloch, Farruh Ishmanov, Yousaf Bin Zikria

This paper improves mispronunciation detection for Arabic words spoken by non-native learners using deep convolutional neural network features, showing stronger performance than handcrafted and transfer-learning baselines.

Ayesha Pervaiz, Fawad Hussain, Huma Israr, Muhammad Ali Tahir, Fawad Riasat Raja, Naveed Khan Baloch, Farruh Ishmanov, Yousaf Bin Zikria

This paper improves speech command recognition under noisy conditions by augmenting training data with noise, demonstrating stronger robustness and lower error rates across clean, noisy, and locally collected speech datasets.

Waqar Amin, Fawad Hussain, Sheraz Anjum, Sarzamin Khan, Naveed Khan Baloch, Zulqar Nain, Sung Won Kim

This paper presents a comparative analysis of application mapping approaches for network-on-chip designs, categorizing current techniques and evaluating their effects on communication cost, power, energy consumption, and latency for real applications such as VOPD and MPEG4.

Naveed Khan Baloch, Muhammad Iram Baig, Masoud Daneshtalab

This paper introduces Defender, a low-overhead fault-tolerant router mechanism for network-on-chip systems, designed to tolerate permanent faults across router components while preserving communication reliability.