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118 results on '"Siddique, Kamran"'

1. Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

Author

Naeem, Muhammad Ahtsam, Saleem, Muhammad Asim, Sharif, Muhammad Imran, Akber, Shahzad, Saleem, Sajjad, Akhtar, Zahid, and Siddique, Kamran

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features.

Published
2025

2. Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods

Author

Hafeez, Rubab, Waheed, Sadia, Naqvi, Syeda Aleena, Maqbool, Fahad, Sarwar, Amna, Saleem, Sajjad, Sharif, Muhammad Imran, Siddique, Kamran, and Akhtar, Zahid

Subjects
Computer Science - Machine Learning
Abstract

Alzheimers disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimers disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4 years after the first clinical indication. Advancements in computing and information technology have led to many techniques of studying Alzheimers disease. Early identification and therapy are crucial for preventing Alzheimers disease, as early-onset dementia hits people before the age of 65, while late-onset dementia occurs after this age. According to the 2015 World Alzheimers disease Report, there are 46.8 million individuals worldwide suffering from dementia, with an anticipated 74.7 million more by 2030 and 131.5 million by 2050. Deep Learning has outperformed conventional Machine Learning techniques by identifying intricate structures in high-dimensional data. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have achieved an accuracy of up to 96.0% for Alzheimers disease classification, and 84.2% for mild cognitive impairment (MCI) conversion prediction. There have been few literature surveys available on applying ML to predict dementia, lacking in congenital observations. However, this survey has focused on a specific data channel for dementia detection. This study evaluated Deep Learning algorithms for early Alzheimers disease detection, using openly accessible datasets, feature segmentation, and classification methods. This article also has identified research gaps and limits in detecting Alzheimers disease, which can inform future research., Comment: 22 pages

Published
2025

3. A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases

Author

Saleem, Sajjad, Hussain, Adil, Majeed, Nabila, Akhtar, Zahid, and Siddique, Kamran

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.

Published
2025
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4. Forecasting Bitcoin Price in Indian Rupees Using Machine Learning Techniques

Author

Siddique, Kamran, Kumar, Pradeep, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, Jaiswal, Ajay, editor, and Kumar, Prabhat, editor

Published
2025
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14. Intrusion Detection in High-Speed Big Data Networks: A Comprehensive Approach

Author

Siddique, Kamran, Akhtar, Zahid, Kim, Yangwoo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Park, James J., editor, Loia, Vincenzo, editor, Yi, Gangman, editor, and Sung, Yunsick, editor

Published
2018
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19. Fast Particle Search and Positioning Algorithms Using an Efficient Cell Registration Method

Author

Ogami, Yoshifumi and Siddique, Kamran

Abstract

Cartesian Cell Registration (CCR) is an effective method to reduce computational time for particle search and positioning, for example, in the Direct Simulation Monte Carlo (DSMC) method for dilute fluid flows and multiphase flow calculations. In this chapter, an efficient FORTRAN algorithm for the CCR method is presented to further reduce both the computational time for registration and computer memory. With this algorithm, the computation time for searching the target cell where the target particle exists is reduced by as much as 44,234 times. Moreover, this algorithm was successfully applied to the DSMC simulation, resulting in a 369-fold reduction in computational time compared to the brute-force approach, that is, searching all target cells until the cell in which the target particle is present is found.

Published
2023

20. Revolutionising Financial Portfolio Management: The Non-Stationary Transformer's Fusion of Macroeconomic Indicators and Sentiment Analysis in a Deep Reinforcement Learning Framework.

Author

Liu, Yuchen, Mikriukov, Daniil, Tjahyadi, Owen Christopher, Li, Gangmin, Payne, Terry R., Yue, Yong, Siddique, Kamran, and Man, Ka Lok

Subjects
REINFORCEMENT learning, DEEP reinforcement learning, PORTFOLIO management (Investments), DEEP learning, TRANSFORMER models, SENTIMENT analysis
Abstract

In the evolving landscape of portfolio management (PM), the fusion of advanced machine learning techniques with traditional financial methodologies has opened new avenues for innovation. Our study introduces a cutting-edge model combining deep reinforcement learning (DRL) with a non-stationary transformer architecture. This model is designed to decode complex patterns in financial time-series data, enhancing portfolio management strategies with deeper insights and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing the model's ability to navigate the complexities of asset management. Rigorous testing demonstrates the model's efficacy, highlighting the benefits of blending diverse data sources and sophisticated algorithmic approaches in mastering the nuances of PM. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Computational Study of a Motion Sensor to Simultaneously Measure Two Physical Quantities in All Three Directions for a UAV.

Author

Siddique, Kamran and Ogami, Yoshifumi

Subjects
*MOTION detectors, *PHYSICAL constants, *DRONE aircraft
Abstract

Cross-axis sensitivity is generally undesirable, and lower values are required for the accurate performance of a thermal accelerometer. In this study, errors in devices are utilized to simultaneously measure two physical quantities of an unmanned aerial vehicle (UAV) in the X-, Y-, and Z-directions, i.e., where three accelerations and three rotations can also be simultaneously measured using a single motion sensor. The 3D structures of thermal accelerometers were designed and simulated in a FEM simulator using commercially available FLUENT 18.2 software Obtained temperature responses were correlated with input physical quantities, and a graphical relationship was created between peak temperature values and input accelerations and rotations. Using this graphical representation, any values of acceleration from 1g to 4g and rotational speed from 200 to 1000°/s can be simultaneously measured in all three directions. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Design and Implementation of Single-cycle MIPS Processor.

Author

Luo Yuanyuan, Qin Chenxi, Zhang Wenyu, and Siddique, Kamran

Subjects
LOGIC circuit design, COMPUTER logic, DIGITAL electronics, DESIGN software
Abstract

Logisim is a digital logic circuit design and simulation software that is an open source, free of charge, secondary development, installation-free, easy to use and intuitive. This paper examines how Logisim can be used to design the data path and combine it with Verilog for FPGA design of single cycle CPUs. [ABSTRACT FROM AUTHOR]

Published
2023

36. An Overview of Adaptive Metadata Prefetching Scheme.

Author

Wu Chengze, Wan Yamei, Li Wenbin, and Siddique, Kamran

Subjects
METADATA, INFORMATION society, CACHE memory, EVERYDAY life
Abstract

The expensive cost and small capacity of cache has made prefetching a major method to close the performance gap between main memory and CPU. Prefetching techniques have increasingly shown their importance as a mean to improve cache hit rates. And Metadata is a kind of significant data, especially in the information age, where massive internet productions based on it are created and used in daily life. In this paper, we first narrate the development of prefetching technologies existed to provide a general impression. And two types of prefetching schemes or algorithms, metadata prefetching and adaptive prefetching are introduced from different perspectives. We then compare and analyze their advantages and present some of their limitations. Then, the further discussion shows how to use the technologies depending on different factors in the real situation, which may be the size of data. Besides, the self-learning, as a popular theory is emphasized here for the choice to be combined together with prefetching technology. [ABSTRACT FROM AUTHOR]

Published
2023

37. Attribute Based Encryption in Cloud Computing.

Author

Shawn Ang, Law Kim Young, Zhi Qi, Akhtar, Zahid, Siddique, Kamran, Ka Lok Man, and Jie Zhang

Subjects
CLOUD computing, INTERNET service providers, DATA privacy, ACCESS control, PUBLIC key cryptography, TRUST
Abstract

Cloud computing is a computing paradigm that provides various services and computing resources to users through Cloud Service Providers over the Internet. However, storing data in an unsecure cloud may lead to security issues such as privacy issues and data leakage. Therefore, it is necessary for encryption schemes to be implemented in clouds to provide a secure environment for the users. One of the cryptographic schemes is the Attribute Based Encryption (ABE), which provides privacy and access control in cloud and can be implemented in a Trusted Real-Time Execution Environment to achieve stronger security. This paper first outlines the various existing encryption techniques, which can be categorized into symmetric and asymmetric algorithms. It then comprehensively explore the key policy and the access policy attribute based encryption. Furthermore, this paper examines the various schemes of ABE and compares the schemes based on its access structure, advantages and disadvantages. Lastly, this paper discusses the applications of key policy and ciphertext policy ABE. [ABSTRACT FROM AUTHOR]

Published
2022

38. Automation in Cloud Migration: An Effective Study.

Author

Rou Lee, Zhi Qi, Akhtar, Zahid, Siddique, Kamran, Ka Lok Man, and Jie Zhang

Subjects
TRUST, AUTOMATION, LABOR supply
Abstract

When conducting a cloud migration in a Trusted Real-Time Execution Environment, it is always important to conduct the migration by following certain standard and constraint while modifying it according to the needs of the migration. Migration framework is introduced to provide such a standard and steps to be followed for migration. However, current migration frameworks no longer essentially satisfy the cloud migration efficiently. Resources are increasing and the usage of current migration frameworks are not proficiently supporting the demands. Thus, researchers are developing more automation cloud migration frameworks that help in reducing the cost, time, manpower and increasing efficiency to conduct cloud migration. This paper concisely addresses cloud migration, stages of conducting cloud migration and introduces various automated cloud migration frameworks along with detailed analysis. [ABSTRACT FROM AUTHOR]

Published
2022

42. Optimizing Small Files Operations in HDFS File Storage Mode.

Author

Yi-Yang Chen, Rui-Jun Wang, Zhen Hong, Akhtar, Zahid, and Siddique, Kamran

Subjects
RECORDS management, STORAGE, BIG data
Abstract

Hadoop Distributed File System (HDFS) is based on Google File System (GFS), a big data distributed file management system included in Hadoop. Nowadays, many HDFS and many other similar frameworks have the need to store small files in the system. In this aspect, HDFS affects its performance and Namenode memory management when dealing with a large number of small files. Therefore, researchers have proposed various solutions to address the shortcomings of HDFS for storing small and medium-sized files. This paper presents three HDFS schemes for merging small files and analyzes the importance of correlation and prefetching after merging small files. The efficiency of reading small files can be improved by correlated file prefetching. Finally, the small file storage architecture is obtained to stand superior to the NHAR architecture. [ABSTRACT FROM AUTHOR]

Published
2022

48. A Review of Mobile Forensic Investigation Process Models

Author

Al-Dhaqm, Arafat, Razak, Shukor Abd, Ikuesan, Richard Adeyemi, Kebande, Victor R., Siddique, Kamran, Al-Dhaqm, Arafat, Razak, Shukor Abd, Ikuesan, Richard Adeyemi, Kebande, Victor R., and Siddique, Kamran

Abstract

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

Published
2020
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49. Towards the Development of an Integrated Incident Response Model for Database Forensic Investigation Field

Author

Al-Dhaqm, Arafat, Razak, Shukor Abd, Siddique, Kamran, Ikuesan, Richard Adeyemi, Kebande, Victor R., Al-Dhaqm, Arafat, Razak, Shukor Abd, Siddique, Kamran, Ikuesan, Richard Adeyemi, and Kebande, Victor R.

Abstract

For every contact that is made in a database, a digital trace will potentially be left and most of the database breaches are mostly aimed at defeating the major security goals (Confidentiality, Integrity, and Authenticity) of data that reside in the database. In order to prove/refute a fact during litigation, it is important to identify suitable investigation techniques that can be used to link a potential incident/suspect to the digital crime. As a result, this paper has proposed suitable steps of constructing and Integrated Incident Response Model (IIRM) that can be relied upon in the database forensic investigation field. While developing the IIRM, design science methodology has been adapted and the outcome of this study has shown significant and promising approaches that could be leveraged by digital forensic experts, legal practitioners and law enforcement agencies. This is owing to the fact, that IIRM construction has followed incident investigation principles that are stipulated in ISO guidelines.

Published
2020
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50. Categorization and Organization of Database Forensic Investigation Processes

Author

Al-Dhaqm, Arafat, Abd Razak, Shukor, Dampier, David A., Choo, Kim-Kwang Raymond, Siddique, Kamran, Ikuesan, Richard Adeyemi, Alqarni, Abdulhadi, Kebande, Victor R., Al-Dhaqm, Arafat, Abd Razak, Shukor, Dampier, David A., Choo, Kim-Kwang Raymond, Siddique, Kamran, Ikuesan, Richard Adeyemi, Alqarni, Abdulhadi, and Kebande, Victor R.

Abstract

Database forensic investigation (DBFI) is an important area of research within digital forensics. It & x2019;s importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.

Published
2020
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