Ali Rizwan - Academia.edu (original) (raw)

Papers by Ali Rizwan

Research paper thumbnail of Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Mathematical Problems in Engineering, May 5, 2022

Research paper thumbnail of Molecular basis of abiotic stress alleviation by nanoparticles

Research paper thumbnail of Addressing Data Sparsity with GANs for Multi-fault Diagnosing in Emerging Cellular Networks

Data-driven machine learning is considered a means to address the paramount challenge of timely f... more Data-driven machine learning is considered a means to address the paramount challenge of timely fault diagnosis in modern and futuristic ultra-dense and highly complex mobile networks. Whereas diagnosing multiple faults in the network at the same time remains an open challenge. In this context, the data sparsity is hindering the potential of machine learning to address such issues. In this work, we have proposed a data augmentation scheme comprising Pix2Pix Generative Adversarial Network (GAN) and a customized loss function never used before, to address the data sparsity challenge in Minimization of Drive Tests (MDT) data. Our proposed unique augmentation scheme generates images of MDT coverage maps with Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values of 25 and 0.97 respectively, which are significantly higher than those achieved without our customized loss function. The performance of data augmentation scheme used is further evaluated with a Convolutional Neural Network (CNN) model for simultaneously detecting most commonly occurring network faults, such as antenna up-tilt, antenna down-tilt, transmission power degradation, and cell outage. The CNN applied on the data generated from the 1% of the MDT data with the proposed augmentation scheme has lead to a gain of 550% in the detection of all classes, including the four faults and cell with normal behavior, as compared to when it is applied on the data generated without our customized loss function.

Research paper thumbnail of Generic Edge Computing System for Optimization and Computation Offloading of Unmanned Aerial Vehicle

Computers & Electrical Engineering, Jul 1, 2023

Research paper thumbnail of Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey

Informatics in Medicine Unlocked, 2022

Research paper thumbnail of Anthelmintic activity of Moringa oleifera and Azadirachta indica against gastrointestinal nematodes of wild sheep

Journal of the Hellenic Veterinary Medical Society, Jul 10, 2022

Gastrointestinal nematodes (GINs) are serious issue for health of wild sheep kept in captivity. C... more Gastrointestinal nematodes (GINs) are serious issue for health of wild sheep kept in captivity. Chemically synthesized anthelmintics are regularly used to control these parasites.In recent years anthelmintic resistance and remnant of drugs in animal products leads to use of medicinal plants as alternative to anthelmintics. In current study, the efficacy of aqueous, methanolic and ethanolic dried leaf extracts of medicinal plants Moringa oleifera and Azadirachta indica were tested for in-vitro ovicidal and larvicidal activities against Haemonchus, Trichuris and Trichostrongylus; naturally acquired nematodes isolated from wild sheep (Ovisorientalisorientalis). Six concentrations of these plants extract (1.56, 3.13, 6.25, 12.5, 25 and 50 mg/ml) were evaluated using egg hatching assay (EHA) and larval development assay (LDA) in three replicates. To compare treatment effects, untreated and treated (0.1% ivermectin) controls were used.The aqueous, methanolic and ethanolic leaf extracts showed anthelmintic activities against isolated genera of nematodes but the inhibition was maximum (99%)in ethanol extract of M. oleifera followed by methanol extract (97%)at maximum concentration tested at (50mg/ml).The overall findings of this study shows that Moringa oleifera and Azadirachta indica leaf extracts possess significant anthelmintic efficacy against GINs of sheep and these could be a natural alternative to synthetic anthelmintics to treat the worm infections in animals.

Research paper thumbnail of ハイブリッド粘性ナノ流体の数学的分数モデルの解析および熱と物質移動におけるその応用【JST・京大機械翻訳】

Journal of Computational and Applied Mathematics, 2021

Research paper thumbnail of Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

IEEE Sensors Journal, Aug 15, 2021

Research paper thumbnail of AI-Assisted Joint Search Approach for Mmwave Cell Discovery Using Sparse MDT Data

IEEE Transactions on Vehicular Technology, 2023

Research paper thumbnail of Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework

IEEE Communications Surveys and Tutorials, 2023

The future of cellular networks is contingent on artificial intelligence (AI) based automation, p... more The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zerotouch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, few-shot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with

Research paper thumbnail of Energy efficient indoor localisation for narrowband internet of things

CAAI Transactions on Intelligence Technology, Feb 17, 2023

There are an increasing number of Narrow Band IoT devices being manufactured as the technology be... more There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%.

Research paper thumbnail of An Optimization Technique for Intrusion Detection of Industrial Control Network Vulnerabilities Based on BP Neural Network

Research Square (Research Square), Nov 30, 2021

An optimization technique for intrusion detection of industrial control network vulnerabilities b... more An optimization technique for intrusion detection of industrial control network vulnerabilities based on BP neural network

Research paper thumbnail of Reduction-responsive and bioorthogonal carboxymethyl cellulose based soft hydrogels cross-linked via IEDDA click chemistry for cancer therapy application

International Journal of Biological Macromolecules, Oct 1, 2022

Research paper thumbnail of Tweet Spam Detection Using Machine Learning and Swarm Optimization Techniques

IEEE Transactions on Computational Social Systems, 2022

Research paper thumbnail of Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

Sensors, Dec 28, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing

Scientific Reports, Mar 8, 2022

Research paper thumbnail of A Deep Learning Network-on-Chip (NoC) based Switch-Router to Enhance Information Security in Resource-Constrained Devices

Journal of Circuits, Systems, and Computers, Jul 28, 2023

Research paper thumbnail of A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports

Research paper thumbnail of Parasitic association of root-knot nematode, meloidogyne incognita on guava

Research paper thumbnail of Machine Learning for Decision Making in Healthcare

This chapter presents a very important use‐case from Rizwan et al. to highlight the role of machi... more This chapter presents a very important use‐case from Rizwan et al. to highlight the role of machine learning in making autonomous decisions for the provision of healthcare services. The scenario presented in this chapter involves use of the data collected for an important bio‐marker, Galvanic Skin Response measured with electrodermal activity sensors, and use of machine learning for auto diagnosis of hydration levels in the human body. The main steps of the bio‐electrical impedance analysis methodology followed in the development of the hydration level detection model are illustrated briefly. Like many real data‐based healthcare studies the main objectives of this chapter are the identification of the appropriate body posture and optimal interval of time for the data collection of bio‐markers and selection of the right combination of features and reliable algorithm for the model development for the auto diagnosis. In the light of the analytical study, the impact of these factors is discussed

Research paper thumbnail of Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Mathematical Problems in Engineering, May 5, 2022

Research paper thumbnail of Molecular basis of abiotic stress alleviation by nanoparticles

Research paper thumbnail of Addressing Data Sparsity with GANs for Multi-fault Diagnosing in Emerging Cellular Networks

Data-driven machine learning is considered a means to address the paramount challenge of timely f... more Data-driven machine learning is considered a means to address the paramount challenge of timely fault diagnosis in modern and futuristic ultra-dense and highly complex mobile networks. Whereas diagnosing multiple faults in the network at the same time remains an open challenge. In this context, the data sparsity is hindering the potential of machine learning to address such issues. In this work, we have proposed a data augmentation scheme comprising Pix2Pix Generative Adversarial Network (GAN) and a customized loss function never used before, to address the data sparsity challenge in Minimization of Drive Tests (MDT) data. Our proposed unique augmentation scheme generates images of MDT coverage maps with Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values of 25 and 0.97 respectively, which are significantly higher than those achieved without our customized loss function. The performance of data augmentation scheme used is further evaluated with a Convolutional Neural Network (CNN) model for simultaneously detecting most commonly occurring network faults, such as antenna up-tilt, antenna down-tilt, transmission power degradation, and cell outage. The CNN applied on the data generated from the 1% of the MDT data with the proposed augmentation scheme has lead to a gain of 550% in the detection of all classes, including the four faults and cell with normal behavior, as compared to when it is applied on the data generated without our customized loss function.

Research paper thumbnail of Generic Edge Computing System for Optimization and Computation Offloading of Unmanned Aerial Vehicle

Computers & Electrical Engineering, Jul 1, 2023

Research paper thumbnail of Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey

Informatics in Medicine Unlocked, 2022

Research paper thumbnail of Anthelmintic activity of Moringa oleifera and Azadirachta indica against gastrointestinal nematodes of wild sheep

Journal of the Hellenic Veterinary Medical Society, Jul 10, 2022

Gastrointestinal nematodes (GINs) are serious issue for health of wild sheep kept in captivity. C... more Gastrointestinal nematodes (GINs) are serious issue for health of wild sheep kept in captivity. Chemically synthesized anthelmintics are regularly used to control these parasites.In recent years anthelmintic resistance and remnant of drugs in animal products leads to use of medicinal plants as alternative to anthelmintics. In current study, the efficacy of aqueous, methanolic and ethanolic dried leaf extracts of medicinal plants Moringa oleifera and Azadirachta indica were tested for in-vitro ovicidal and larvicidal activities against Haemonchus, Trichuris and Trichostrongylus; naturally acquired nematodes isolated from wild sheep (Ovisorientalisorientalis). Six concentrations of these plants extract (1.56, 3.13, 6.25, 12.5, 25 and 50 mg/ml) were evaluated using egg hatching assay (EHA) and larval development assay (LDA) in three replicates. To compare treatment effects, untreated and treated (0.1% ivermectin) controls were used.The aqueous, methanolic and ethanolic leaf extracts showed anthelmintic activities against isolated genera of nematodes but the inhibition was maximum (99%)in ethanol extract of M. oleifera followed by methanol extract (97%)at maximum concentration tested at (50mg/ml).The overall findings of this study shows that Moringa oleifera and Azadirachta indica leaf extracts possess significant anthelmintic efficacy against GINs of sheep and these could be a natural alternative to synthetic anthelmintics to treat the worm infections in animals.

Research paper thumbnail of ハイブリッド粘性ナノ流体の数学的分数モデルの解析および熱と物質移動におけるその応用【JST・京大機械翻訳】

Journal of Computational and Applied Mathematics, 2021

Research paper thumbnail of Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

IEEE Sensors Journal, Aug 15, 2021

Research paper thumbnail of AI-Assisted Joint Search Approach for Mmwave Cell Discovery Using Sparse MDT Data

IEEE Transactions on Vehicular Technology, 2023

Research paper thumbnail of Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework

IEEE Communications Surveys and Tutorials, 2023

The future of cellular networks is contingent on artificial intelligence (AI) based automation, p... more The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zerotouch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, few-shot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with

Research paper thumbnail of Energy efficient indoor localisation for narrowband internet of things

CAAI Transactions on Intelligence Technology, Feb 17, 2023

There are an increasing number of Narrow Band IoT devices being manufactured as the technology be... more There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%.

Research paper thumbnail of An Optimization Technique for Intrusion Detection of Industrial Control Network Vulnerabilities Based on BP Neural Network

Research Square (Research Square), Nov 30, 2021

An optimization technique for intrusion detection of industrial control network vulnerabilities b... more An optimization technique for intrusion detection of industrial control network vulnerabilities based on BP neural network

Research paper thumbnail of Reduction-responsive and bioorthogonal carboxymethyl cellulose based soft hydrogels cross-linked via IEDDA click chemistry for cancer therapy application

International Journal of Biological Macromolecules, Oct 1, 2022

Research paper thumbnail of Tweet Spam Detection Using Machine Learning and Swarm Optimization Techniques

IEEE Transactions on Computational Social Systems, 2022

Research paper thumbnail of Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

Sensors, Dec 28, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing

Scientific Reports, Mar 8, 2022

Research paper thumbnail of A Deep Learning Network-on-Chip (NoC) based Switch-Router to Enhance Information Security in Resource-Constrained Devices

Journal of Circuits, Systems, and Computers, Jul 28, 2023

Research paper thumbnail of A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports

Research paper thumbnail of Parasitic association of root-knot nematode, meloidogyne incognita on guava

Research paper thumbnail of Machine Learning for Decision Making in Healthcare

This chapter presents a very important use‐case from Rizwan et al. to highlight the role of machi... more This chapter presents a very important use‐case from Rizwan et al. to highlight the role of machine learning in making autonomous decisions for the provision of healthcare services. The scenario presented in this chapter involves use of the data collected for an important bio‐marker, Galvanic Skin Response measured with electrodermal activity sensors, and use of machine learning for auto diagnosis of hydration levels in the human body. The main steps of the bio‐electrical impedance analysis methodology followed in the development of the hydration level detection model are illustrated briefly. Like many real data‐based healthcare studies the main objectives of this chapter are the identification of the appropriate body posture and optimal interval of time for the data collection of bio‐markers and selection of the right combination of features and reliable algorithm for the model development for the auto diagnosis. In the light of the analytical study, the impact of these factors is discussed