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Conference Object Citation - WoS: 3Biomechanical Design and Control of Lower Limb Exoskeleton for Sit-to-Stand and Stand-to-Sit Movements(Ieee, 2018) Qureshi, Muhammad Hamza; Masood, Zeeshan; Rehman, Linta; Owais, Muhammad; Khan, Muhammad UmerIn this paper, we present design and development phase of lower limb robotic exoskeleton that can assist paralyzed individuals. Motion of the human wearing exoskeleton is introduced by actuators. Both exoskeleton legs are attached to the supporting frame by passive universal joints. The exoskeleton provides 3 DOFs per limb of which two joints are active and one passive. The control actions i.e., sit-to-stand and stand-to-sit movements are triggered using Double Pole Double Throw (DPDT) toggle switch. The control scheme is implement using Switch control method and the feedback is provided by means of current measurement. This assistive device can be utilized for the disabled persons. The simulation results are provided that evaluates the performance of the control actions on exoskeleton.Article Performance Investigation of ML Algorithms for Potato Blight Classification: The Role of Hyperparameter Tuning(Springer, 2026) Saeed, Sadia; Rehman, Hafiz Zia Ur; Hussain, Muhammad Ureed; Khan, Muhammad Umer; Saeed, Muhammad TallalPotato is the world's fourth most important food crop, consumed by over a billion people. Early and late blight diseases can reduce yields by up to 40%, leading to severe economic and food security challenges. While manual detection methods are prone to error, automated, image-based machine learning (ML) offers a promising alternative, though its performance depends strongly on proper optimization. This study investigates the role of hyperparameter tuning in improving ML algorithms for potato blight classification. We utilized two datasets: the PlantVillage dataset (500 images per class) and a region-specific Potato Leaf Dataset (PLD) from Pakistan (1628 early blight, 1424 late blight, 1020 healthy). All images were resized to 256 & times; 256 pixels and augmented. Features were extracted using the Bag-of-Features (BoF) technique, and four classic ML models-Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)-were trained. Hyperparameters were optimized via grid search with 5-fold cross-validation. This tuning led to measurable improvements; for instance, SVM accuracy increased from 93.0% to 95.9% on PlantVillage and from 85.0% to 87.0% on PLD. Evaluation using precision, recall, F1-score, and specificity confirmed SVM as the best-performing model. A confusion matrix analysis revealed that most misclassifications occurred between the two blight types due to visual similarity. To translate our findings into practice, we developed a MATLAB Graphical User Interface (GUI) that enables farmers to classify a leaf image in under three seconds and receive precautionary recommendations. This study demonstrates that systematic hyperparameter optimization is crucial for maximizing ML performance and is a key step in building accessible, real-time tools for precision agriculture. Future work will focus on extending the system to mobile and web platforms.
