sheeraz Arif - Academia.edu (original) (raw)

Papers by sheeraz Arif

Computers in Biology and Medicine, Mar 1, 2022

Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can ... more Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can significantly improve lives of millions of women across the globe. In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

arXiv (Cornell University), Oct 3, 2021

Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can ... more Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can significantly improve lives of millions of women across the globe. In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

Biomedical Signal Processing and Control, Sep 1, 2023

Research in Agricultural Engineering

Weed identification and controlling systems are gaining great attention and are very effective fo... more Weed identification and controlling systems are gaining great attention and are very effective for large productivity in the agriculture sector. Currently, farmers are facing a weed control and management problem, and to tackle this challenge precision agriculture in the form of selective spraying is much-needed practice. In this article, we introduce a novel framework for a weed identification system that leverages (hybrid) the robust and relevant features of deep learning models, such as convolutional neural network (CNN) and handcrafted features. First, we apply the image preprocessing and augmentation techniques for image quality and dataset size enhancement. Then, we apply handcrafted feature extraction techniques, such as local binary pattern (LBP) and histogram of oriented gradients (HOG) to extract texture and shape features from the input. We also apply the deep learning model, such as CNN, to capture the relevant semantic features. Lastly, we concatenate the features extracted from a different domain and explore the performance using different classifiers. We achieved better performance and classification accuracy in the presence of the extreme gradient boosting (XGBoost) classifier. The achieved results witnessed the effectiveness and applicability of the given method and the importance of concatenated features.

2023 International Conference on Communication, Computing and Digital Systems (C-CODE)

JISR on Computing, Dec 16, 2022

KSII Transactions on Internet and Information Systems, 2019

Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field ... more Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.

Breast Cancer, 2020

Background To compare the breast cancer detection performance in digital mammograms of a panel of... more Background To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women. Methods The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions. Results The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively. Conclusions Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.

Breast cancer is one of the leading causes of death among women across the globe. It is difficult... more Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art.

Computers, Materials & Continua, 2022

The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of... more The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of IoT networks. Side Channel Attacks (SCA) are one of the major concerns. The prime objective of SCA is to acquire the information by observing the power consumption, electromagnetic (EM) field, timing analysis, and acoustics of the device. Later, the attackers perform statistical functions to recover the key. Advanced Encryption Standard (AES) algorithm has proved to be a good security solution for constrained IoT devices. This paper implements a simulation model which is used to modify the AES algorithm using logical masking properties. This invariant of the AES algorithm hides the array of bits during substitution byte transformation of AES. This model is used against SCA and particularly Power Analysis Attacks (PAAs). Simulation model is designed on MATLAB simulator. Results will give better solution by hiding power profiles of the IoT devices against PAAs. In future, the lightweight AES algorithm with false key mechanisms and power reduction techniques such as wave dynamic differential logic (WDDL) will be used to safeguard IoT devices against side channel attacks by using Arduino and field programmable gate array (FPGA).

Computers in Biology and Medicine, 2022

Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can ... more Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can significantly improve lives of millions of women across the globe. In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

The smart agricultural robotic system can decrease the dependence on various traditional agricult... more The smart agricultural robotic system can decrease the dependence on various traditional agriculture crop spraying methods such as pesticides, herbicides, and fertilizer. To meet the world population food requirements, conventional schemes are not sufficient for spraying agrochemicals to control the weeds and increase crop production. Therefore, a smart and intelligent farming system is introduced to increase the production of crops and to reach crop production target. In this paper, Deep Learning (DL) based algorithms is applied for the identification and classification of weed plants using combination of Convolutional Neural Networks (CNN) and Long-Short- Term Memory (LSTM). Convolutional Neural Networks (CNN) has a unique structure to get discriminative features for the input images, and LSTM allows to jointly optimize the classification. To validate the proposed scheme, nine kinds of weeds are classified using the proposed method such as vine weeds, three-leaf weeds, spiky weeds...

The main purpose of this research is to highlight the problems and advantages of both the power c... more The main purpose of this research is to highlight the problems and advantages of both the power control mechanism and compare them accordingly. It has been investigated that open loop system is faster as compared to the close loop system as it has no long procedure or computations. It just have to compare the powers of MS (mobile station) and BS (base station) and increase / decrease the power. The present research mainly focuses on the comparison of WCDMA power control methods including, open and close loop methods, it further focus on channel transmission and signal-to-interference ratio (SIR). The channels used in controlling of power of WCDMA mobile networks are also investigated. Open loop power control has the limitations as it uses the same properties for both the directions of transmission as this is not the case every time practically or on the field. On the other hand close loop power control mechanism has a delay of 1/1.5 kHz (667 microseconds) but it is reliable because ...

2021 International Conference on Computer & Information Sciences (ICCOINS), 2021

Internet-of-Things (IoT) establish connection between billions of smart devices, performing a div... more Internet-of-Things (IoT) establish connection between billions of smart devices, performing a diverse range of purposes. These devices embedded with sensors, software and network to exchange and collect data with each other. In IoT hardware security is the main task for researchers because large number of devices connected day by day with each other. As researchers concern, latest research is now focused on seeing attack and defensive site of IoT devices. Side channel attacks are one of the major concerns for researchers who work on IoT. This paper presents an implementation of AES algorithm with proposed false key mechanism and power reduction technique against SCA in IoT devices.

2021 International Conference on Computer & Information Sciences (ICCOINS), 2021

In the World of Communication, digital transmission has its importance in sense of speed and accu... more In the World of Communication, digital transmission has its importance in sense of speed and accuracy which is the basic requirement of current time. Nowadays, successful as well as rapid communication is the main concern of the research. It can be achieved by using different technique and methods based on theories built upon the branches of applied sciences and engineering. In this research an algorithm is presented by sequentially combining two transforms, Wavelet and Krylov. The Algorithm was formerly developed by the same and was known as WK Algorithm. In this research the Algorithm is first studied for digital signal application and results are presented and concluded by applying also with other two methods in order to verify and validate the research.

2017 International Conference on Virtual Reality and Visualization (ICVRV), 2017

In this paper, we present a simple method to integrate the discriminative information of video fo... more In this paper, we present a simple method to integrate the discriminative information of video for the action recognition tasks. We introduce the concept of motion map to represent the prefix of video sequences by optimizing the recognition accuracy of original video. 3-dimensional convolution (3Dconv) based model is used to generate the new motion map by integrating current motion map and future video frame. This model is capable of increasing the length of training video in iterative manner and allow us to generate the final motion map. Experimental evaluation results on widely used datasets i.e HMDB51 and UCF101 have revealed effectiveness and flexibility of proposed method over other baseline schemes.

Wireless Communications and Mobile Computing, 2021

Existing attribute learning methods rely on predefined attributes, which require manual annotatio... more Existing attribute learning methods rely on predefined attributes, which require manual annotations. Due to the limitation of human experience, the predefined attributes are not capable enough of providing enough description. This paper proposes a self-supervised attribute learning (SAL) method, which automatically generates attribute descriptions by differentially occluding the object region to deal with the above problems. The relationship between attributes is formulated with triplet loss functions and is utilized to supervise the CNN. Attribute learning is used as an auxiliary task of a multitask image classification and segmentation network, in which self-supervision of attributes motivates the CNN to learn more discriminative features for the main semantic tasks. Experimental results on public benchmarks CUB-2011 and Pascal VOC show that the proposed SAL-Net can obtain more accurate classification and segmentation results without additional annotations. Moreover, the SAL-Net i...

Computers in Biology and Medicine, Mar 1, 2022

Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can ... more Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can significantly improve lives of millions of women across the globe. In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

arXiv (Cornell University), Oct 3, 2021

Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can ... more Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can significantly improve lives of millions of women across the globe. In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

Biomedical Signal Processing and Control, Sep 1, 2023

Research in Agricultural Engineering

Weed identification and controlling systems are gaining great attention and are very effective fo... more Weed identification and controlling systems are gaining great attention and are very effective for large productivity in the agriculture sector. Currently, farmers are facing a weed control and management problem, and to tackle this challenge precision agriculture in the form of selective spraying is much-needed practice. In this article, we introduce a novel framework for a weed identification system that leverages (hybrid) the robust and relevant features of deep learning models, such as convolutional neural network (CNN) and handcrafted features. First, we apply the image preprocessing and augmentation techniques for image quality and dataset size enhancement. Then, we apply handcrafted feature extraction techniques, such as local binary pattern (LBP) and histogram of oriented gradients (HOG) to extract texture and shape features from the input. We also apply the deep learning model, such as CNN, to capture the relevant semantic features. Lastly, we concatenate the features extracted from a different domain and explore the performance using different classifiers. We achieved better performance and classification accuracy in the presence of the extreme gradient boosting (XGBoost) classifier. The achieved results witnessed the effectiveness and applicability of the given method and the importance of concatenated features.

2023 International Conference on Communication, Computing and Digital Systems (C-CODE)

JISR on Computing, Dec 16, 2022

KSII Transactions on Internet and Information Systems, 2019

Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field ... more Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.

Breast Cancer, 2020

Background To compare the breast cancer detection performance in digital mammograms of a panel of... more Background To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women. Methods The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions. Results The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively. Conclusions Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.

Breast cancer is one of the leading causes of death among women across the globe. It is difficult... more Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art.

Computers, Materials & Continua, 2022

The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of... more The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of IoT networks. Side Channel Attacks (SCA) are one of the major concerns. The prime objective of SCA is to acquire the information by observing the power consumption, electromagnetic (EM) field, timing analysis, and acoustics of the device. Later, the attackers perform statistical functions to recover the key. Advanced Encryption Standard (AES) algorithm has proved to be a good security solution for constrained IoT devices. This paper implements a simulation model which is used to modify the AES algorithm using logical masking properties. This invariant of the AES algorithm hides the array of bits during substitution byte transformation of AES. This model is used against SCA and particularly Power Analysis Attacks (PAAs). Simulation model is designed on MATLAB simulator. Results will give better solution by hiding power profiles of the IoT devices against PAAs. In future, the lightweight AES algorithm with false key mechanisms and power reduction techniques such as wave dynamic differential logic (WDDL) will be used to safeguard IoT devices against side channel attacks by using Arduino and field programmable gate array (FPGA).

Computers in Biology and Medicine, 2022

Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can ... more Breast cancer is one of leading cause of death among women. Early diagnosis of breast cancer can significantly improve lives of millions of women across the globe. In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

The smart agricultural robotic system can decrease the dependence on various traditional agricult... more The smart agricultural robotic system can decrease the dependence on various traditional agriculture crop spraying methods such as pesticides, herbicides, and fertilizer. To meet the world population food requirements, conventional schemes are not sufficient for spraying agrochemicals to control the weeds and increase crop production. Therefore, a smart and intelligent farming system is introduced to increase the production of crops and to reach crop production target. In this paper, Deep Learning (DL) based algorithms is applied for the identification and classification of weed plants using combination of Convolutional Neural Networks (CNN) and Long-Short- Term Memory (LSTM). Convolutional Neural Networks (CNN) has a unique structure to get discriminative features for the input images, and LSTM allows to jointly optimize the classification. To validate the proposed scheme, nine kinds of weeds are classified using the proposed method such as vine weeds, three-leaf weeds, spiky weeds...

The main purpose of this research is to highlight the problems and advantages of both the power c... more The main purpose of this research is to highlight the problems and advantages of both the power control mechanism and compare them accordingly. It has been investigated that open loop system is faster as compared to the close loop system as it has no long procedure or computations. It just have to compare the powers of MS (mobile station) and BS (base station) and increase / decrease the power. The present research mainly focuses on the comparison of WCDMA power control methods including, open and close loop methods, it further focus on channel transmission and signal-to-interference ratio (SIR). The channels used in controlling of power of WCDMA mobile networks are also investigated. Open loop power control has the limitations as it uses the same properties for both the directions of transmission as this is not the case every time practically or on the field. On the other hand close loop power control mechanism has a delay of 1/1.5 kHz (667 microseconds) but it is reliable because ...

2021 International Conference on Computer & Information Sciences (ICCOINS), 2021

Internet-of-Things (IoT) establish connection between billions of smart devices, performing a div... more Internet-of-Things (IoT) establish connection between billions of smart devices, performing a diverse range of purposes. These devices embedded with sensors, software and network to exchange and collect data with each other. In IoT hardware security is the main task for researchers because large number of devices connected day by day with each other. As researchers concern, latest research is now focused on seeing attack and defensive site of IoT devices. Side channel attacks are one of the major concerns for researchers who work on IoT. This paper presents an implementation of AES algorithm with proposed false key mechanism and power reduction technique against SCA in IoT devices.

2021 International Conference on Computer & Information Sciences (ICCOINS), 2021

In the World of Communication, digital transmission has its importance in sense of speed and accu... more In the World of Communication, digital transmission has its importance in sense of speed and accuracy which is the basic requirement of current time. Nowadays, successful as well as rapid communication is the main concern of the research. It can be achieved by using different technique and methods based on theories built upon the branches of applied sciences and engineering. In this research an algorithm is presented by sequentially combining two transforms, Wavelet and Krylov. The Algorithm was formerly developed by the same and was known as WK Algorithm. In this research the Algorithm is first studied for digital signal application and results are presented and concluded by applying also with other two methods in order to verify and validate the research.

2017 International Conference on Virtual Reality and Visualization (ICVRV), 2017

In this paper, we present a simple method to integrate the discriminative information of video fo... more In this paper, we present a simple method to integrate the discriminative information of video for the action recognition tasks. We introduce the concept of motion map to represent the prefix of video sequences by optimizing the recognition accuracy of original video. 3-dimensional convolution (3Dconv) based model is used to generate the new motion map by integrating current motion map and future video frame. This model is capable of increasing the length of training video in iterative manner and allow us to generate the final motion map. Experimental evaluation results on widely used datasets i.e HMDB51 and UCF101 have revealed effectiveness and flexibility of proposed method over other baseline schemes.

Wireless Communications and Mobile Computing, 2021

Existing attribute learning methods rely on predefined attributes, which require manual annotatio... more Existing attribute learning methods rely on predefined attributes, which require manual annotations. Due to the limitation of human experience, the predefined attributes are not capable enough of providing enough description. This paper proposes a self-supervised attribute learning (SAL) method, which automatically generates attribute descriptions by differentially occluding the object region to deal with the above problems. The relationship between attributes is formulated with triplet loss functions and is utilized to supervise the CNN. Attribute learning is used as an auxiliary task of a multitask image classification and segmentation network, in which self-supervision of attributes motivates the CNN to learn more discriminative features for the main semantic tasks. Experimental results on public benchmarks CUB-2011 and Pascal VOC show that the proposed SAL-Net can obtain more accurate classification and segmentation results without additional annotations. Moreover, the SAL-Net i...