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Faheem Akhtar
Department of Computer Science, Sukkur IBA University, Sukkur 65200, Pakistan

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Research article
Published: 28 August 2021 in Security and Communication Networks
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Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.

ACS Style

Sirajuddin Qureshi; Jianqiang Li; Faheem Akhtar; Saima Tunio; Zahid Hussain Khand; Ahsan Wajahat. Analysis of Challenges in Modern Network Forensic Framework. Security and Communication Networks 2021, 2021, 1 -13.

AMA Style

Sirajuddin Qureshi, Jianqiang Li, Faheem Akhtar, Saima Tunio, Zahid Hussain Khand, Ahsan Wajahat. Analysis of Challenges in Modern Network Forensic Framework. Security and Communication Networks. 2021; 2021 ():1-13.

Chicago/Turabian Style

Sirajuddin Qureshi; Jianqiang Li; Faheem Akhtar; Saima Tunio; Zahid Hussain Khand; Ahsan Wajahat. 2021. "Analysis of Challenges in Modern Network Forensic Framework." Security and Communication Networks 2021, no. : 1-13.

Article
Published: 03 August 2021 in The Journal of Supercomputing
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The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network’s weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network’s existence. Consequently, it extends the network’s lifetime, overcomes the data transmission’s energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure.

ACS Style

Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Suhail Ashfaq Butt; Allah Ditta; Sirajuddin Qureshi. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing 2021, 1 -30.

AMA Style

Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Suhail Ashfaq Butt, Allah Ditta, Sirajuddin Qureshi. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing. 2021; ():1-30.

Chicago/Turabian Style

Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Suhail Ashfaq Butt; Allah Ditta; Sirajuddin Qureshi. 2021. "An intelligent fault detection approach based on reinforcement learning system in wireless sensor network." The Journal of Supercomputing , no. : 1-30.

Journal article
Published: 17 May 2021 in IEEE Access
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The astonishing growth of sophisticated ever-evolving cyber threats and attacks throws the entire Internet-of-Things (IoT) infrastructure into chaos. As the IoT belongs to the infrastructure of interconnected devices, it brings along significant security challenges. Cyber threat analysis is an augmentation of a network security infrastructure that primarily emphasizes on detection and prevention of sophisticated network-based threats and attacks. Moreover, it requires the security of network by investigation and classification of malicious activities. In this study, we propose a DL-enabled malware detection scheme using a hybrid technique based on the combination of a Deep Neural Network(DNN) and Long Short-Term Memory(LSTM) for the efficient identification of multi-class malware families in IoT infrastructure. The proposed scheme utilizes latest 2018 dataset named as N_BaIoT. Furthermore, our proposed scheme is evaluated using standard performance metrics such as accuracy, recall, precision, F1-score, and so forth. The DL-based malware detection system achieves 99.96% detection accuracy for IoT based threats. Finally, we also compare our proposed work with other robust and state-of-the-art detection schemes.

ACS Style

Sirajuddin Qureshi; Jingsha He; Saima Tunio; Nafei Zhu; Faheem Akhtar; Faheem Ullah; Ahsan Nazir; Ahsan Wajahat. A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks. IEEE Access 2021, 9, 73938 -73947.

AMA Style

Sirajuddin Qureshi, Jingsha He, Saima Tunio, Nafei Zhu, Faheem Akhtar, Faheem Ullah, Ahsan Nazir, Ahsan Wajahat. A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks. IEEE Access. 2021; 9 ():73938-73947.

Chicago/Turabian Style

Sirajuddin Qureshi; Jingsha He; Saima Tunio; Nafei Zhu; Faheem Akhtar; Faheem Ullah; Ahsan Nazir; Ahsan Wajahat. 2021. "A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks." IEEE Access 9, no. : 73938-73947.

Journal article
Published: 23 November 2020 in IEEE Access
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This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.

ACS Style

Qing Zhao; Guohong Yao; Faheem Akhtar; Jianqiang Li; Yan Pei. An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods. IEEE Access 2020, 8, 223335 -223345.

AMA Style

Qing Zhao, Guohong Yao, Faheem Akhtar, Jianqiang Li, Yan Pei. An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods. IEEE Access. 2020; 8 (99):223335-223345.

Chicago/Turabian Style

Qing Zhao; Guohong Yao; Faheem Akhtar; Jianqiang Li; Yan Pei. 2020. "An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods." IEEE Access 8, no. 99: 223335-223345.

Journal article
Published: 02 September 2020 in IEEE Access
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Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false- (positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

ACS Style

Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access 2020, 8, 165779 -165809.

AMA Style

Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Azhar Imran, Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access. 2020; 8 (99):165779-165809.

Chicago/Turabian Style

Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. 2020. "A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities." IEEE Access 8, no. 99: 165779-165809.

Research article
Published: 17 August 2020 in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
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Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases.

ACS Style

Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020, 8, 691 -698.

AMA Style

Azhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Ji-Jiang Yang, Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2020; 8 (6):691-698.

Chicago/Turabian Style

Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. 2020. "Automated identification of cataract severity using retinal fundus images." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 8, no. 6: 691-698.

Article
Published: 10 June 2020 in Multimedia Tools and Applications
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In recent years, a rapid rise in the incidence of Large for gestational age (LGA) neonate is reported, and health care professionals employed themselves to discover the cause. Utmost of the previous studies were cohort or observational studies that employed simple linear or multivariate regression models, and very few of them employed machine learning (ML) schemes. Therefore, this research proposes to use 1 expert-driven and 7 automated feature selection schemes with well-known ML classifiers using 10 and 30 folds cross-validation. The induced results were compared with existing baselines, and Wilcoxon signed-rank test and the Friedman test were also introduced to verify the results. The ranked 20 features of the proposed expert-driven feature selection scheme outperformed amongst 7 automated feature selection schemes with a prediction precision, accuracy, and AUC scores of 0.94606, 0.84529, and 0.86492, respectively. Out of twenty features, eleven features were found similar to twenty ranked features of the automated feature selection schemes subsets. The classification results of the extracted features were utmost identical to the results of twenty features subset proposed by the expert-driven feature selection scheme. Therefore, we suggest pediatricians to refresh LGA diagnosis process with the proposed scheme because of its practical usage and maximum expert involvement.

ACS Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications 2020, 79, 34047 -34077.

AMA Style

Faheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications. 2020; 79 (45-46):34047-34077.

Chicago/Turabian Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. 2020. "Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data." Multimedia Tools and Applications 79, no. 45-46: 34047-34077.

Conference paper
Published: 01 May 2020 in 2020 Prognostics and Health Management Conference (PHM-Besançon)
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Thalassemia is an inherited blood disorder caused by abnormal production of hemoglobin. In order to establish an efficient thalassemia prognosis process, the practitioners suggest using complete blood count (CBC) report. Based on CBC report, practitioners often use professional experience and domain knowledge to discover the cause and relevant risk factors. Thus, to the best of our knowledge, this research is the first that uses machine learning techniques to accurately classify and predict thalassemia patients using the parameters of CBC report. WBC, RBC, HB, HCT, Platelets and an additional parameter Ferritin (Iron) are the selected parameters for the experimentations. The experimental analysis of the results show that RBC, HB, and Ferritin (Iron) plays a vital role in the establishment of an efficient thalassemia prognosis process.

ACS Style

Faheem Akhtar; Anum Shakeel; Jianqiang Li; Yan Pei; Yanping Dang. Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis. 2020 Prognostics and Health Management Conference (PHM-Besançon) 2020, 1 -7.

AMA Style

Faheem Akhtar, Anum Shakeel, Jianqiang Li, Yan Pei, Yanping Dang. Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis. 2020 Prognostics and Health Management Conference (PHM-Besançon). 2020; ():1-7.

Chicago/Turabian Style

Faheem Akhtar; Anum Shakeel; Jianqiang Li; Yan Pei; Yanping Dang. 2020. "Risk Factors Selection for Predicting Thalassemia Patients using Linear Discriminant Analysis." 2020 Prognostics and Health Management Conference (PHM-Besançon) , no. : 1-7.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
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In the large for gestational age infant’s classification and prediction, noisy features are distilled to improve the classifier performance. It is accomplished with the creation of a suitable feature vector followed by GridSearch-based Recursive Feature Elimination with Cross-Validation (RFECV) scheme. It attempts to elect features that are influential and independent. We executed experiments on the data obtained from the National Pregnancy and Examination Program of China (2010–2013). The results are compared with the results already reported in the literature. The GridSearch-based RFECV scheme exhibited smaller features subset size with an increased classifier performance. The precision and area under the curve (AUC) scores are drastically improved from 0.7134 and 0.7074 to 0.96 to 0.86 respectively. Therefore, pediatricians are suggested to use fifty-three features subset, ranked by GridSearch-based RFECV scheme using Support Vector Machine (SVM) for the establishment of an efficient LGA prognosis process.

ACS Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Yang Xu; Asif Rajput; Qing Wang. Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme. Lecture Notes in Electrical Engineering 2020, 63 -71.

AMA Style

Faheem Akhtar, Jianqiang Li, Yan Pei, Yang Xu, Asif Rajput, Qing Wang. Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme. Lecture Notes in Electrical Engineering. 2020; ():63-71.

Chicago/Turabian Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Yang Xu; Asif Rajput; Qing Wang. 2020. "Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme." Lecture Notes in Electrical Engineering , no. : 63-71.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
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Gestational weight is an essential parameter for the pediatrician to clinically evaluate the health of both neonate and the mother. During the last several decades, an increase in the prevalence of Large for Gestational Age (LGA) neonate is reported and several researchers engaged themselves to discover the cause. Most of them conducted observational or retrospective studies that used simple statistical test (i.e. univariate/multivariate logistic regression etc.,). However, machine learning schemes are rarely been employed to discover the cause. In this research, one proposed expert-driven and seven automated feature selection schemes with five well-known machine learning classifiers using (10 & 30)-fold cross-validations are employed for the establishment of an efficient and accurate LGA classification model. Accuracy, precision, and AUC scores are selected for the evaluation of the proposed scheme. Wilcoxon signed rank, friedman, and bonferroni-dunn tests are used to observe the variations among (10 & 30)-fold cross validation results and to rank various feature selection and classification schemes. Two baseline methods are also used to compare the results of the proposed expert-driven feature selection scheme. The top 20 features selected by the proposed expert-driven feature selection scheme outperformed among seven automated feature selection schemes. A comparison analysis is also performed between expert-driven and data-driven feature subsets. Furthermore, with the intersection of proposed expert-driven and data-driven feature subsets, it is foreseen that out of 20 features, 11 features are found similar, which authenticates the proposed scheme. The classification performance of the 11 extracted features is almost similar to the proposed expert-driven feature selection scheme. Ensemble technique is also exploited to build the better and effective LGA classification model.

ACS Style

Guohong Yao; Jianqiang Li; Yan Pei; Faheem Akhtar; Bo Liu. An Automatic Turner Syndrome Identification System with Facial Images. Lecture Notes in Electrical Engineering 2020, 105 -112.

AMA Style

Guohong Yao, Jianqiang Li, Yan Pei, Faheem Akhtar, Bo Liu. An Automatic Turner Syndrome Identification System with Facial Images. Lecture Notes in Electrical Engineering. 2020; ():105-112.

Chicago/Turabian Style

Guohong Yao; Jianqiang Li; Yan Pei; Faheem Akhtar; Bo Liu. 2020. "An Automatic Turner Syndrome Identification System with Facial Images." Lecture Notes in Electrical Engineering , no. : 105-112.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
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Arguably the rapid development of Internet financial is one of the most significant breakthroughs in the financial domain. Automated financial statistics have gradually substituted the traditional manual statistical methods, providing a reliable data basis for economic planning. Therefore, the quality of a business activity heavily relies on the accuracy analysis of user preferences and recommend rated products to the users. Traditional item-based collaborative filtering method plays a dominant role for analyzing user preference and recommending the items for users, this method mainly utilize the fully rating data to predict whether the user like the target item. However, in many cases, the available user rating data is sparsely, which makes traditional item-based collaborative filtering method inefficient and inapplicable. To address this problem, this paper propose an ontology-based user preference statistical model (ontology-based UPS), where the concept and attribute features are extracted from financial ontology for semantic similarity computing; later, it is combined with the calculated rating similarities to improve the accuracy of the similar item set for the target item. The research results show that our approach outperformed traditional collaborative filtering method.

ACS Style

Yuxi Chen; Xiaotong Zhang; Qing Zhao; Faheem Akhtar; Ting Yang; Ke Huang; Jun Li; Qing Wang. An Ontology Based Approach for User Preference Statistics. Lecture Notes in Electrical Engineering 2020, 352 -361.

AMA Style

Yuxi Chen, Xiaotong Zhang, Qing Zhao, Faheem Akhtar, Ting Yang, Ke Huang, Jun Li, Qing Wang. An Ontology Based Approach for User Preference Statistics. Lecture Notes in Electrical Engineering. 2020; ():352-361.

Chicago/Turabian Style

Yuxi Chen; Xiaotong Zhang; Qing Zhao; Faheem Akhtar; Ting Yang; Ke Huang; Jun Li; Qing Wang. 2020. "An Ontology Based Approach for User Preference Statistics." Lecture Notes in Electrical Engineering , no. : 352-361.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
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We propose a cluster-based feature selection (CFS) scheme to establish an efficient prognosis process for the identification of a Macrosomia fetus. Macrosomia fetus adheres numerous complications during and after the antepartum period and is among established reasons for neonate mortality. Almost all of the classifiers with the proposed CFS scheme elevated macrosomia prediction scores compare to previously published studies. The prediction scores are increased by \(+4\%\) and \(+12\%\) in terms of precision and Area under the curve which authenticates the applied scheme. Therefore, we suggest pediatricians use CFS scheme with Support Vector Machine (SVM) for developing better prognosis process to develop the best macrosomia prediction framework.

ACS Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Shafaq Siraj; Zeeshan Shaukat. Macrosomia Fetus Prediction with Cluster-Based Feature Selection Scheme. Lecture Notes in Electrical Engineering 2020, 55 -62.

AMA Style

Faheem Akhtar, Jianqiang Li, Yan Pei, Shafaq Siraj, Zeeshan Shaukat. Macrosomia Fetus Prediction with Cluster-Based Feature Selection Scheme. Lecture Notes in Electrical Engineering. 2020; ():55-62.

Chicago/Turabian Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Shafaq Siraj; Zeeshan Shaukat. 2020. "Macrosomia Fetus Prediction with Cluster-Based Feature Selection Scheme." Lecture Notes in Electrical Engineering , no. : 55-62.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
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Turner syndrome adheres serious health-related complications with a tendency to affect various organs during different stages of life which includes hypertension, infertility, and retarded growth. The proper diagnosis of TS requires an expensive test named karyotype test which is not easily available in remote health care units in the countryside. Therefore, we proposed to use facial images to detect TS to pursue a higher accuracy of recognition. The proposed scheme achieved the accuracy of 91.3% with mixed feature extraction schemes using thirty principle components selected with criteria that retained 95% of the information from the turner dataset. Moreover, this research is the first that uses facial features to accurately diagnose TS patients and has the capability to help doctors to establish a cost-effective TS prognosis process in remote health care units that lack required health care facilities.

ACS Style

Xiang Gao; Jianqiang Li; Yan Pei; Faheem Akhtar; Qing Wang; Ting Yang; Ke Huang; Jun Li; Ji-Jiang Yang. Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes. Lecture Notes in Electrical Engineering 2020, 72 -78.

AMA Style

Xiang Gao, Jianqiang Li, Yan Pei, Faheem Akhtar, Qing Wang, Ting Yang, Ke Huang, Jun Li, Ji-Jiang Yang. Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes. Lecture Notes in Electrical Engineering. 2020; ():72-78.

Chicago/Turabian Style

Xiang Gao; Jianqiang Li; Yan Pei; Faheem Akhtar; Qing Wang; Ting Yang; Ke Huang; Jun Li; Ji-Jiang Yang. 2020. "Turner Syndrome Prognosis with Facial Features Extraction and Selection Schemes." Lecture Notes in Electrical Engineering , no. : 72-78.

Journal article
Published: 01 January 2020 in Journal of Physics: Conference Series
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With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

ACS Style

Yuxi Chen; Xiaotong Zhang; Qing Zhao; Faheem Akhtar. A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation. Journal of Physics: Conference Series 2020, 1453, 1 .

AMA Style

Yuxi Chen, Xiaotong Zhang, Qing Zhao, Faheem Akhtar. A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation. Journal of Physics: Conference Series. 2020; 1453 ():1.

Chicago/Turabian Style

Yuxi Chen; Xiaotong Zhang; Qing Zhao; Faheem Akhtar. 2020. "A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation." Journal of Physics: Conference Series 1453, no. : 1.

Journal article
Published: 01 January 2020 in International Journal of Electronic Security and Digital Forensics
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International publishers of academic, scientific and professional journals since 1979.

ACS Style

Muhammad Azeem; Jingsha He; Allah Ditta; Faheem Akhtar. A Novel Approach to Secret Data Concealment with High Cover Text Capacity and Security. International Journal of Electronic Security and Digital Forensics 2020, 12, 1 .

AMA Style

Muhammad Azeem, Jingsha He, Allah Ditta, Faheem Akhtar. A Novel Approach to Secret Data Concealment with High Cover Text Capacity and Security. International Journal of Electronic Security and Digital Forensics. 2020; 12 (1):1.

Chicago/Turabian Style

Muhammad Azeem; Jingsha He; Allah Ditta; Faheem Akhtar. 2020. "A Novel Approach to Secret Data Concealment with High Cover Text Capacity and Security." International Journal of Electronic Security and Digital Forensics 12, no. 1: 1.

Conference paper
Published: 01 January 2020 in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
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Nowadays, a tremendous amount of data is produced every single day by computational systems and electronic instruments, whether its an online transaction or data manipulation over the internet, security has dependably remained the best concern in any correspondence communication system. The amount of information to be exchanged is not the problem. The essential variable is the Channel, through which the data is transferred, ought to be secured. So in terms of secure transmission of data, the best technique usually used these days is cryptography. Cryptography is a procedure of making data incomprehensible to an unapproved individual. Different cryptographic algorithms can be utilized in a very efficient manner. In a perfect world, a user needs the best cryptographic algorithm with low cost and high in performance measure. So for the critical needs of the user, the cryptographic algorithm chose the best algorithm amongst existing algorithms. This research paper presents a neat and clean study of various cryptographic algorithms (DES, 3DES, AES, RSA) based on the computational complexity and performance measure.

ACS Style

Khalid Ali; Faheem Akhtar; Suhail Ahmed Memon; Anum Shakeel; Asif Ali; Abdul Raheem. Performance of Cryptographic Algorithms based on Time Complexity. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -5.

AMA Style

Khalid Ali, Faheem Akhtar, Suhail Ahmed Memon, Anum Shakeel, Asif Ali, Abdul Raheem. Performance of Cryptographic Algorithms based on Time Complexity. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-5.

Chicago/Turabian Style

Khalid Ali; Faheem Akhtar; Suhail Ahmed Memon; Anum Shakeel; Asif Ali; Abdul Raheem. 2020. "Performance of Cryptographic Algorithms based on Time Complexity." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-5.

Conference paper
Published: 01 January 2020 in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
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In recent times social media has become the most important part of everyone to connect with global world to share the information. Generally, Social media is used for multi-purposes such as reading newspaper, magazines and books. The actual involvement, i.e. likes and share of the post of social media is providing the specific interests of community. The information which we extract from social networking websites requires criteria for better understanding the essential factors and their importance. The main focus of this research is on the visualization of social network for an understanding of the involvement of the community. All the measures are set for specific profiles that use media houses and magazine fan pages, likes and favorites. In this study, we have used our personal Facebook profile to extract the data to visualize in the Gephi tool. It is useful here to point out the importance for Netvizz application that is a part of Facebook API. Simulation results show essential factors such as centrality measures for the social network mainly Facebook.

ACS Style

Ahsan Wajahat; Ahsan Nazir; Faheem Akhtar; Sirajuddin Qureshi; Faheem Ullah; Fahad Razaque; Anum Shakeel. Interactively Visualize and Analyze Social Network Gephi. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -9.

AMA Style

Ahsan Wajahat, Ahsan Nazir, Faheem Akhtar, Sirajuddin Qureshi, Faheem Ullah, Fahad Razaque, Anum Shakeel. Interactively Visualize and Analyze Social Network Gephi. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-9.

Chicago/Turabian Style

Ahsan Wajahat; Ahsan Nazir; Faheem Akhtar; Sirajuddin Qureshi; Faheem Ullah; Fahad Razaque; Anum Shakeel. 2020. "Interactively Visualize and Analyze Social Network Gephi." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-9.

Conference paper
Published: 01 January 2020 in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
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The consumption of energy in buildings has risen abruptly over the last decades. Due to less energy-efficient buildings, most of the energy is being thrown in our surroundings thus making an adverse effect on our environment. In this paper, heating and cooling loads of private or non-commercial buildings are covered. By implementing the proposed technique, which is a blend of cluster analysis and Artificial Neural Network (ANN), evaluation and prediction are performed. The estimation of heating and cooling loads of private or non-commercial buildings are performed using eight input variables in the ANN-based model. The details of variables are as follows, a relative surface area, total height, compactness, roof area, glazing area distribution, orientation, glazing area, and wall area. K-means clustering methodology is then used to cluster buildings on the basis of output variables. Stand on simulated literature data, evaluation of 768 different private or non-commercial buildings is done using the above-suggested method. Research results depicted that depending upon input variables, the above-suggested approach can efficiently evaluate heating and cooling load that is very much close to real test results.

ACS Style

Ahsan Nazir; Ahsan Wajahat; Faheem Akhtar; Faheem Ullah; Sirajuddin Qureshi; Sher Afghan Malik; Anum Shakeel. Evaluating Energy Efficiency of Buildings using Artificial Neural Networks and K-means Clustering Techniques. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -7.

AMA Style

Ahsan Nazir, Ahsan Wajahat, Faheem Akhtar, Faheem Ullah, Sirajuddin Qureshi, Sher Afghan Malik, Anum Shakeel. Evaluating Energy Efficiency of Buildings using Artificial Neural Networks and K-means Clustering Techniques. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-7.

Chicago/Turabian Style

Ahsan Nazir; Ahsan Wajahat; Faheem Akhtar; Faheem Ullah; Sirajuddin Qureshi; Sher Afghan Malik; Anum Shakeel. 2020. "Evaluating Energy Efficiency of Buildings using Artificial Neural Networks and K-means Clustering Techniques." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-7.

Conference paper
Published: 01 January 2020 in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
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The objective of this study is to discretize the crime rate figures into categorical values and to find associations between different crime rates in multiple cities. Regression modeling is used to regress the value of one crime rate on the basis of all others or some of the other crimes rates. As per US statistics on crime, this research focused on identifying the variable crime in multiple cities of the US and categorized the data of crime by applying machine learning techniques with the establishment of data discretization and preprocessing process.

ACS Style

Suhail Ahmed Memon; Faheem Akhtar; Ahsan Nazir; Ahsan Wajahat; Sirajuddin Qureshi; Faheem Ullah; Anum Shakeel. Discretization of the Crime Rate from Numerical Into Categorical. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -7.

AMA Style

Suhail Ahmed Memon, Faheem Akhtar, Ahsan Nazir, Ahsan Wajahat, Sirajuddin Qureshi, Faheem Ullah, Anum Shakeel. Discretization of the Crime Rate from Numerical Into Categorical. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-7.

Chicago/Turabian Style

Suhail Ahmed Memon; Faheem Akhtar; Ahsan Nazir; Ahsan Wajahat; Sirajuddin Qureshi; Faheem Ullah; Anum Shakeel. 2020. "Discretization of the Crime Rate from Numerical Into Categorical." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-7.

Journal article
Published: 01 January 2020 in International Journal of Communication Networks and Distributed Systems
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Exploration of advanced technologies employed for streamlining UWSN devices is a hot issue. Challenges faced by UWSN are high power consumption, increased propagation latency and dynamic topology of nodes. Depth-based routing protocols are designed to cope these challenges that use depth information to forward data toward surface sink. Depth-based routing and energy efficient depth-based routing are widely used routing protocols in UWSN. DBR has low stability period due to un-necessary data forwarding and high load on mid and low-depth nodes. In DBR quick energy consumption of nodes creates large holes in the network. The path connectivity hole leads retransmission of packets that consumes excessive energy, route-update cost and end-to-end delay. In this paper, the proposed robust depth-based routing protocol is used to overcome deficiencies of DBR by limiting number of forwarding nodes considering variation in depth-threshold, holding-time and distance-differences to minimise end-to-end delay.

ACS Style

Tariq Mahmood; Faheem Akhtar; Khalil Ur Rehman; Muhammad Azeem; Azhar Imran Mudassir; Sher Daudpota. Introducing robustness in DBR routing protocol. International Journal of Communication Networks and Distributed Systems 2020, 24, 316 .

AMA Style

Tariq Mahmood, Faheem Akhtar, Khalil Ur Rehman, Muhammad Azeem, Azhar Imran Mudassir, Sher Daudpota. Introducing robustness in DBR routing protocol. International Journal of Communication Networks and Distributed Systems. 2020; 24 (3):316.

Chicago/Turabian Style

Tariq Mahmood; Faheem Akhtar; Khalil Ur Rehman; Muhammad Azeem; Azhar Imran Mudassir; Sher Daudpota. 2020. "Introducing robustness in DBR routing protocol." International Journal of Communication Networks and Distributed Systems 24, no. 3: 316.