113 research outputs found
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No full textZahoor Hussain Chohan-Editorial; Riaz Hussain-Article-Muhammad (SAW) and the People of India and Pakistan. pp. 7-11; Ahsan Waseem-Poetry-The Land of the Pious. pp. 11; Sultan Khan-Article-Quaid as a Political Philosopher. pp. 12-14; Aniza Zaheer-Article-Building-Up the National Fiber. pp. 15-19; Muhammad Tanvir-Essay-Importance of Discipline. pp. 19-20; Hameed Nizamee, Edited by Saleem Mansur Khalid-Article-Iqbal and his Urdu Poetry a General Survey Based on his Urdu Works. pp. 21-33; Amjad Ali Bhatti-Essay-The Prophet's Message to the Muslims. pp. 34-36; Arif Qureshi-Poetry-Salute to the Quaid. pp. 36; Tahir Kamran-Essay-Rise of Muslim History Writing. pp. 37-39; Muhammad Ahsan Pasha-Essay-Where do we Stand. pp. 40-42; Irshad-ul-Hasan-Poetry-The Walls of Glass. pp. 43-44; Rubina Nazir Chohan-Poetry-Gift. pp. 44; Sohail Ahmad Sharyar-Essay-What Is Literature. pp. 45-47; Muhammad Ahsan Pasha-Poetry-A Funny Commentary on Chaucer. pp. 47-48; Irshad-ul-Hasan-Article-Symbolism. pp. 49-61; Gilani Kamran-Essay-Discovering Folklore. pp. 62-64; Mohammad Tanvir Butt-Essay-Father of the Nation. pp. 65-67; Tariq Hameed Rathore-Poetry-Pleasant Manner. pp. 68; Syed Saadat Mehdi-The Days at College. pp. 68-69; Muhammad Akmal-Three Narrations. pp. 69-70; Wasif Rashid-Friendship & Friends. pp. 71; Ateeq-ur-Rahman-May You Have. pp. 71; Ali Awais-Quotes. pp. 72; Hammad Raza-Definitions. pp. 72; Akhlaque Ahmad-Mind and its Problems. pp. 73-74; Kamran Mahboob-Notice for the Students. pp. 75; Sana-ur-Rahman-Article-Emancipation of Women. pp. 76-80; Dalip Kumar Rajpoot-Best Use of Youth. pp. 80; Akhtar Ali Khan-Essay-Way to Economic Progress. pp. 81-82; Zia-ul-Haq-Essay-The World First Democracy. pp. 82-83; Zahor Hussain Chohan-On the Eve of Retirement. pp. 84; Folio [Urdu]. 272 p.College Buildings. after English title; Quaid-e-Azam. after contents; Allama Iqbal. 1 page after contents; Dr Shaukat Ali, Principal. 2 pages after contents; Prof Talat Mahmood. 3 pages after contents; Students Union 1988-1989. after page 84; Editorial Board 1988-89. after editorial Urdu pag
A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables
No full textDeep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method
A Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand
No full textDeep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.FALS
Phytopharmacological evaluation of different solvent extract/fractions From<i> Sphaeranthus indicus</i> L. flowers:From traditional therapies to bioactive compounds
No full textSphaeranthus indicus L. is a medicinal herb having widespread traditional uses for treating common ailments. The present research work aims to explore the in-depth phytochemical composition and in vitro reactivity of six different polarity solvents (methanol, n-hexane, benzene, chloroform, ethyl acetate, and n-butanol) extracts/fractions of S. indicus flowers. The phytochemical composition was accomplished by determining total bioactive contents, HPLC-PDA polyphenolic quantification, and UHPLC-MS secondary metabolomics. The reactivity of the phenolic compounds was tested through the following biochemical assays: antioxidant (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum, and metal chelation) and enzyme inhibition (AChE, BChE, α-glucosidase, α-amylase, urease, and tyrosinase) assays were performed. The methanol extract showed the highest values for phenolic (94.07 mg GAE/g extract) and flavonoid (78.7 mg QE/g extract) contents and was also the most active for α-glucosidase inhibition as well as radical scavenging and reducing power potential. HPLC-PDA analysis quantified rutin, naringenin, chlorogenic acid, 3-hydroxybenzoic acid, gallic acid, and epicatechin in a significant amount. UHPLC-MS analysis of methanol and ethyl acetate extracts revealed the presence of well-known phytocompounds; most of these were phenolic, flavonoid, and glycoside derivatives. The ethyl acetate fraction exhibited the highest inhibition against tyrosinase and urease, while the n-hexane fraction was most active for α-amylase. Moreover, principal component analysis highlighted the positive correlation between bioactive compounds and the tested extracts. Overall, S. indicus flower extracts were found to contain important phytochemicals, hence could be further explored to discover novel bioactive compounds that could be a valid starting point for future pharmaceutical and nutraceuticals applications.</p
Phenolic profiling and in vitro biological properties of two Lamiaceae species (Salvia modesta and Thymus argaeus): A comprehensive evaluation
No full textThe genus Salvia and Thymus have gained much popularity as an alternative therapy in Turkish folk medicine for abdominal pain, cold, nausea, among others. Nonetheless, some species are yet to be further explored for their bioactivities. We investigated the biological activities of 3 extracts (dichloromethane, methanol, and water (decoction)) of Salvia modesta Boiss. and Thymus argaeus (Fisch. & C.A.Mey.) Boiss. & Balansa. based on antioxidant and enzyme inhibition along with the determination of polyphenolic content. Antioxidant potential was assessed using six assays namely: 2,2-diphenyl-1-picrylhydrazyl, 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), cupric ion reducing antioxidant capacity, ferric reducing antioxidant power, phosphomolybdenum, and metal chelating. Moreover, enzyme inhibition activities of the extracts were studied against acetylcholinesterase, butyrylcholinesterase, tyrosinase, a-amylase, and a-glucosidase. Results revealed that the decoction of both plants was the strongest antioxidants. The methanol extracts displayed the highest tyrosinase inhibition while the dichloromethane extracts of both plants were the most effective butyrylcholinesterase and a-glucosidase inhibitors. In addition, the total phenolic and total flavonoid content was highest in the decoction and methanolic extract of Thymus argaeus, respectively. The most abundant phenolic compound was rosmarinic acid (6574 mu g/g and 5390 mu g/g in T. argaeus and S. modesta methanolic extracts, respectively). PASS prediction analysis revealed that chlorogenic acid showed the highest Pa value for antioxidant activity (0.809) including the mechanism of free radical scavenging (0.856), while rosmarinic acid showed the highest Pa value (0.798) for antidiabetic activity. To conclude, both Salvia modesta and Thymus argaeus can be regarded as new sources of antioxidants and enzyme inhibitors to manage oxidative stress and their complications
Adaptive Swin Transformer V2-Tiny Based Model for Classification of Bacteria, Fungus, Virus, and Healthy Fruit and Leaf Images
No full textThe classification of fruits and leaves affected by bacteria, viruses, and fungi has made significant progress in the fields of artificial intelligence and image processing. However, most methods focus on particular categories of fruit and leaf diseases, but not on both fruit and leaf diseases caused by bacteria, viruses, and fungi. This study aimed to develop a model for the classification of the initial, intermediate, and final stages of bacterial, viral, and fungal diseases, irrespective of fruit and leaf types. To achieve this goal, inspired by the accomplishments of the Swin Transformer, the Swin Transformer V2-Tiny was explored for the classification of 10 classes, which included healthy and three stages of bacteria, virus, and fungus images of fruits and leaves. The stages of Swin Transformer V2-Tiny divide the image into patches, namely, linear projection, Window Multi-Head Self-Attention (W-MSA), and Shifted Window Multi-Head Self-Attention (SW-MSA) for local and global features, which were adapted to perform the plant disease classification. Experiments on authors’ curated and standard datasets and a comparative study with recent methods demonstrate effective classification and superiority over existing methods. To the best of our knowledge, this is the first study on the classification of fruit and leaf pathogens caused by bacteria, viruses, and fungi based on their development stages. The proposed model achieved an average classification rate of 91.04% on fruit datasets and 94.07% on leaf datasets, outperforming recent benchmark methods. It also demonstrated strong generalization on unseen public datasets with over 93% accuracy. Received: 5 May 2025 | Revised: 15 August 2025 | Accepted: 17 October 2025 Conflicts of Interest Shivakumara Palaiahnakote is the Editor-in-Chief for Artificial Intelligence and Applications, and he was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Poornima Basatti Hanuma Gowda: Software, Data curation, Writing – original draft, Visualization. Basavanna Mahadevappa: Formal analysis, Investigation, Supervision, Project administration. Shivakumara Palaiahnakote: Conceptualization, Methodology. Muhammad Hammad Saleem: Validation, Writing – review & editing. Niranjan Mallappa Hanumanthu: Resources
Deep learning-based approaches for plant disease and weed detection : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand
No full textListed in 2023 Dean's List of Exceptional ThesesTo match the ever-growing food demand, the scientific community has been actively focusing on addressing the various challenges faced by the agricultural sector. The major challenges are soil infertility, abrupt changes in climatic conditions, scarcity of water, untrained labor, emission of greenhouse gases, and many others. Moreover, plant diseases and weeds are two of the most important agricultural problems that reduce crop yield. Therefore, accurate detection of plant diseases and weeds is one of the essential operations to apply targeted and timely control measures. As a result, this can improve crop productivity, reduce the environmental effects and financial losses resulting from the excessive application of fungicide/herbicide spray on diseased plants/weeds. Among various ways of plant disease and weed detection, image-based methods are significantly effective for the interpretation of the distinct features. In recent years, image-based deep learning (DL) techniques have been reported in literature for the recognition of weeds and plant diseases. However, the full potential of DL has not yet been explored as most of the methods rely on modifications of the DL models for well-known and readily available datasets. The current studies lack in several ways, such as addressing various complex agricultural conditions, exploring several aspects of DL, and providing a systematic DL-based approach.
To address these research gaps, this thesis presents various DL-based methodologies and aims to improve the mean average precision (mAP) for the identification of diseases and weeds in several plant species. The research on plant disease recognition starts with a publicly available dataset called PlantVillage and comparative analyses are conducted on various DL feature extractors, meta-architectures, and optimization algorithms. Later, new datasets are generated from various local New Zealand horticultural farms, named NZDLPlantDisease-v1 & v2. The proposed datasets consist of healthy and diseased plant organs of 13 economically important horticultural crops of New Zealand, divided into 48 classes. A performance-optimized DL model and a transfer learning-based approach are proposed for the detection of plant diseases using curated datasets. The weed identification has been performed on an open-source dataset called DeepWeeds. A two-step weed detection pipeline is presented to show the performance improvement of the deep learning model with a significant margin. The results for both agricultural tasks achieve superior performance compared to the existing method/default settings. The research outcomes elaborate the practical aspects and extended potential of DL for selected agricultural applications. Therefore, this thesis is a benchmark step for cost-effective crop protection and site-specific weed management systems (SSWM)
Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers
No full textRecently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes
Istihsan (juristic preference) : the forgotten principle of Islamic law
No full textEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Common Fixed Points Technique for Existence of a Solution of Urysohn Type Integral Equations System in Complex Valued <i>b</i>-Metric Spaces
No full textIn this paper we give some common fixed point theorems for Ćirić type operators in complex valued b-metric spaces. Also, some corollaries under this contraction condition are obtained. Our results extend and generalize the results of Hammad et al. In the second part of the paper, in order to strengthen our main results, an illustrative example and some applications are given
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