Pub Date : 2024-04-01DOI: 10.33166/aetic.2024.02.002
Y. Yanagi, R. Orihara, Yasuyuki Tahara, Y. Sei, Tanel Alumäe, Akihiko Ohsuga
In recent times, advancements in text-to-speech technologies have yielded more natural-sounding voices. However, this has also made it easier to generate malicious fake voices and disseminate false narratives. ASVspoof stands out as a prominent benchmark in the ongoing effort to automatically detect fake voices, thereby playing a crucial role in countering illicit access to biometric systems. Consequently, there is a growing need to broaden our perspectives, particularly when it comes to detecting fake voices on social media platforms. Moreover, existing detection models commonly face challenges related to their generalization performance. This study sheds light on specific instances involving the latest speech generation models. Furthermore, we introduce a novel framework designed to address the nuances of detecting fake voices in the context of social media. This framework considers not only the voice waveform but also the speech content. Our experiments have demonstrated that the proposed framework considerably enhances classification performance, as evidenced by the reduction in equal error rate. This underscores the importance of considering the waveform and the content of the voice when tasked with identifying fake voices and disseminating false claims.
{"title":"The Proposal of Countermeasures for DeepFake Voices on Social Media Considering Waveform and Text Embedding","authors":"Y. Yanagi, R. Orihara, Yasuyuki Tahara, Y. Sei, Tanel Alumäe, Akihiko Ohsuga","doi":"10.33166/aetic.2024.02.002","DOIUrl":"https://doi.org/10.33166/aetic.2024.02.002","url":null,"abstract":"In recent times, advancements in text-to-speech technologies have yielded more natural-sounding voices. However, this has also made it easier to generate malicious fake voices and disseminate false narratives. ASVspoof stands out as a prominent benchmark in the ongoing effort to automatically detect fake voices, thereby playing a crucial role in countering illicit access to biometric systems. Consequently, there is a growing need to broaden our perspectives, particularly when it comes to detecting fake voices on social media platforms. Moreover, existing detection models commonly face challenges related to their generalization performance. This study sheds light on specific instances involving the latest speech generation models. Furthermore, we introduce a novel framework designed to address the nuances of detecting fake voices in the context of social media. This framework considers not only the voice waveform but also the speech content. Our experiments have demonstrated that the proposed framework considerably enhances classification performance, as evidenced by the reduction in equal error rate. This underscores the importance of considering the waveform and the content of the voice when tasked with identifying fake voices and disseminating false claims.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"1612 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.33166/aetic.2024.02.003
Muhammad Khubayeeb Kabir, Kawshik Kumar Ghosh, Md. Fahim Ul Islam, Jia Uddin
Wildfires are a widespread phenomenon that affects every corner of the world with the warming climate. Wildfires burn tens of thousands of square kilometres of forests and vegetation every year in the United States alone with the past decade witnessing a dramatic increase in the number of wildfire incidents. This research aims to understand the regions of forests and vegetation across the US that are susceptible to wildfires using spatiotemporal kernel heat maps and, forecast these wildfires across the United States at country-wide and state levels on a weekly and monthly basis in an attempt to reduce the reaction time of the suppression operations and effectively design resource maps to mitigate wildfires. We employed the state-of-the-art Neural Basis Expansion Analysis for Time Series (N-BEATS) model to predict the total area burned by wildfires by several weeks and months into the future. The model was evaluated based on forecasting metrics including mean-squared error (MSE)., and mean average error (MAE). The N-BEATS model demonstrates improved performance compared to other state-of-the-art (SOTA) models, obtaining MSE values of 116.3, 38.2, and 19.0 for yearly, monthly, and weekly forecasting, respectively.
{"title":"Wildfire Prediction in the United States Using Time Series Forecasting Models","authors":"Muhammad Khubayeeb Kabir, Kawshik Kumar Ghosh, Md. Fahim Ul Islam, Jia Uddin","doi":"10.33166/aetic.2024.02.003","DOIUrl":"https://doi.org/10.33166/aetic.2024.02.003","url":null,"abstract":"Wildfires are a widespread phenomenon that affects every corner of the world with the warming climate. Wildfires burn tens of thousands of square kilometres of forests and vegetation every year in the United States alone with the past decade witnessing a dramatic increase in the number of wildfire incidents. This research aims to understand the regions of forests and vegetation across the US that are susceptible to wildfires using spatiotemporal kernel heat maps and, forecast these wildfires across the United States at country-wide and state levels on a weekly and monthly basis in an attempt to reduce the reaction time of the suppression operations and effectively design resource maps to mitigate wildfires. We employed the state-of-the-art Neural Basis Expansion Analysis for Time Series (N-BEATS) model to predict the total area burned by wildfires by several weeks and months into the future. The model was evaluated based on forecasting metrics including mean-squared error (MSE)., and mean average error (MAE). The N-BEATS model demonstrates improved performance compared to other state-of-the-art (SOTA) models, obtaining MSE values of 116.3, 38.2, and 19.0 for yearly, monthly, and weekly forecasting, respectively.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"59 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.33166/aetic.2024.02.004
A. Akinwale, John E. Efiong, E. A. Olajubu, G. A. Aderounmu
Mobile wireless networks enable the connection of devices to a network with minimal or no infrastructure. This comes with the advantages of ease and cost-effectiveness, thus largely popularizing the network. Notwithstanding these merits, the open physical media, infrastructural-less attributes, and pervasive deployment of wireless networks make the channel of communication (media access) vulnerable to attacks such as traffic analysis, monitoring, and jamming. This study designed a virtual local area network (VLAN) model to circumvent virtual jamming attacks and other intrusions at the Media Access Control (MAC) layer of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. A Torpor VLAN (TVLAN) Data Frame Encapsulation and the algorithm for T-VLAN security in CSMA/CA were formulated and presented. A simulation experiment was conducted on the model using OMNeT++ software. The performance metrics used to evaluate the model were packet delivery ratio, network throughput, end-to-end channel delay, and channel load. The simulation results show that the TVLAN defence mechanism did not increase the channel load arbitrarily during TVLAN defence. similarly, the system throughput was shown to be 82% during TVLAN defence. Nevertheless, the network delay of the system during TVLAN defence was significantly high but the channel load was 297 when the TVLAN security mechanism was launched. These results demonstrate the model’s ability to provide a survivability mechanism for critical systems when under attack and add a security layer to the CSMA/CA protocol in wireless networks. Such a remarkable performance is required of a CSMA/CA infrastructure for improving the cybersecurity posture of a wireless network.
{"title":"A Torpor-based Enhanced Security Model for CSMA/CA Protocol in Wireless Networks","authors":"A. Akinwale, John E. Efiong, E. A. Olajubu, G. A. Aderounmu","doi":"10.33166/aetic.2024.02.004","DOIUrl":"https://doi.org/10.33166/aetic.2024.02.004","url":null,"abstract":"Mobile wireless networks enable the connection of devices to a network with minimal or no infrastructure. This comes with the advantages of ease and cost-effectiveness, thus largely popularizing the network. Notwithstanding these merits, the open physical media, infrastructural-less attributes, and pervasive deployment of wireless networks make the channel of communication (media access) vulnerable to attacks such as traffic analysis, monitoring, and jamming. This study designed a virtual local area network (VLAN) model to circumvent virtual jamming attacks and other intrusions at the Media Access Control (MAC) layer of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. A Torpor VLAN (TVLAN) Data Frame Encapsulation and the algorithm for T-VLAN security in CSMA/CA were formulated and presented. A simulation experiment was conducted on the model using OMNeT++ software. The performance metrics used to evaluate the model were packet delivery ratio, network throughput, end-to-end channel delay, and channel load. The simulation results show that the TVLAN defence mechanism did not increase the channel load arbitrarily during TVLAN defence. similarly, the system throughput was shown to be 82% during TVLAN defence. Nevertheless, the network delay of the system during TVLAN defence was significantly high but the channel load was 297 when the TVLAN security mechanism was launched. These results demonstrate the model’s ability to provide a survivability mechanism for critical systems when under attack and add a security layer to the CSMA/CA protocol in wireless networks. Such a remarkable performance is required of a CSMA/CA infrastructure for improving the cybersecurity posture of a wireless network.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"49 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.33166/AETiC.2024.02.005
Saleem Alzoubi, Mahdi H. Miraz
Enhancing robot navigation efficiency is a crucial objective in modern robotics. Robots relying on external navigation systems are often susceptible to electromagnetic interference (EMI) and encounter environmental disturbances, resulting in orientation errors within their surroundings. Therefore, the study employed an internal navigation system to enhance robot navigation efficacy under interference conditions, based on the analysis of the internal parameters and the external signals. This article presents details of the robot’s autonomous operation, which allows for setting the robot's trajectory using an embedded map. The robot’s navigation process involves counting the number of wheel revolutions as well as adjusting wheel orientation after each straight path section. In this article, an autonomous robot navigation system has been presented that leverages an embedded control navigation map utilising cellular automata with active cells which can effectively navigate in an environment containing various types of obstacles. By analysing the neighbouring cells of the active cell, the cellular environment determines which cell should become active during the robot’s next movement step. This approach ensures the robot’s independence from external control inputs. Furthermore, the accuracy and speed of the robot’s movement have been further enhanced using a hexagonal mosaic for navigation surface mapping. This concept of utilising on cellular automata with active cells has been extended to the navigation of a group of robots on a shared navigation surface, taking into account the intersections of the robots’ trajectories over time. To achieve this, a distance control module has been used that records the travelled trajectories in terms of wheel turns and revolutions.
{"title":"Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells","authors":"Saleem Alzoubi, Mahdi H. Miraz","doi":"10.33166/AETiC.2024.02.005","DOIUrl":"https://doi.org/10.33166/AETiC.2024.02.005","url":null,"abstract":"Enhancing robot navigation efficiency is a crucial objective in modern robotics. Robots relying on external navigation systems are often susceptible to electromagnetic interference (EMI) and encounter environmental disturbances, resulting in orientation errors within their surroundings. Therefore, the study employed an internal navigation system to enhance robot navigation efficacy under interference conditions, based on the analysis of the internal parameters and the external signals. This article presents details of the robot’s autonomous operation, which allows for setting the robot's trajectory using an embedded map. The robot’s navigation process involves counting the number of wheel revolutions as well as adjusting wheel orientation after each straight path section. In this article, an autonomous robot navigation system has been presented that leverages an embedded control navigation map utilising cellular automata with active cells which can effectively navigate in an environment containing various types of obstacles. By analysing the neighbouring cells of the active cell, the cellular environment determines which cell should become active during the robot’s next movement step. This approach ensures the robot’s independence from external control inputs. Furthermore, the accuracy and speed of the robot’s movement have been further enhanced using a hexagonal mosaic for navigation surface mapping. This concept of utilising on cellular automata with active cells has been extended to the navigation of a group of robots on a shared navigation surface, taking into account the intersections of the robots’ trajectories over time. To achieve this, a distance control module has been used that records the travelled trajectories in terms of wheel turns and revolutions.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.33166/aetic.2024.02.001
Sincy John, A. Danti
In the realm of deep learning, the prevalence of models with large number of parameters poses a significant challenge for low computation device. Critical influence of model size, primarily governed by weight parameters in shaping the computational demands of the occlusion removal process. Recognizing the computational burdens associated with existing occlusion removal algorithms, characterized by their propensity for substantial computational resources and large model sizes, we advocate for a paradigm shift towards solutions conducive to low-computation environments. Existing occlusion riddance techniques typically demand substantial computational resources and storage capacity. To support real-time applications, it's imperative to deploy trained models on resource-constrained devices like handheld devices and internet of things (IoT) devices possess limited memory and computational capabilities. There arises a critical need to compress and accelerate these models for deployment on resource-constrained devices, without compromising significantly on model accuracy. Our study introduces a significant contribution in the form of a compressed model designed specifically for addressing occlusion in face images for low computation devices. We perform dynamic quantization technique by reducing the weights of the Pix2pix generator model. The trained model is then compressed, which significantly reduces its size and execution time. The proposed model, is lightweight, due to storage space requirement reduced drastically with significant improvement in the execution time. The performance of the proposed method has been compared with other state of the art methods in terms of PSNR and SSIM. Hence the proposed lightweight model is more suitable for the real time applications with less computational cost.
{"title":"Lightweight Model for Occlusion Removal from Face Images","authors":"Sincy John, A. Danti","doi":"10.33166/aetic.2024.02.001","DOIUrl":"https://doi.org/10.33166/aetic.2024.02.001","url":null,"abstract":"In the realm of deep learning, the prevalence of models with large number of parameters poses a significant challenge for low computation device. Critical influence of model size, primarily governed by weight parameters in shaping the computational demands of the occlusion removal process. Recognizing the computational burdens associated with existing occlusion removal algorithms, characterized by their propensity for substantial computational resources and large model sizes, we advocate for a paradigm shift towards solutions conducive to low-computation environments. Existing occlusion riddance techniques typically demand substantial computational resources and storage capacity. To support real-time applications, it's imperative to deploy trained models on resource-constrained devices like handheld devices and internet of things (IoT) devices possess limited memory and computational capabilities. There arises a critical need to compress and accelerate these models for deployment on resource-constrained devices, without compromising significantly on model accuracy. Our study introduces a significant contribution in the form of a compressed model designed specifically for addressing occlusion in face images for low computation devices. We perform dynamic quantization technique by reducing the weights of the Pix2pix generator model. The trained model is then compressed, which significantly reduces its size and execution time. The proposed model, is lightweight, due to storage space requirement reduced drastically with significant improvement in the execution time. The performance of the proposed method has been compared with other state of the art methods in terms of PSNR and SSIM. Hence the proposed lightweight model is more suitable for the real time applications with less computational cost.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"181 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speech is essential to human communication; therefore, distinguishing it from noise is crucial. Speech separation becomes challenging in real-world circumstances with background noise and overlapping speech. Moreover, the speech separation using short-term Fourier transform (STFT) and discrete wavelet transform (DWT) addresses time and frequency resolution and time-variation issues, respectively. To solve the above issues, a new speech separation technique is presented based on the double-density dual-tree complex wavelet transform (DDDTCWT) and sparse non-negative matrix factorization (SNMF). The signal is separated into high-pass and low-pass frequency components using DDDTCWT wavelet decomposition. For this analysis, we only considered the low-pass frequency components and zeroed out the high-pass ones. Subsequently, the STFT is then applied to each sub-band signal to generate a complex spectrogram. Therefore, we have used SNMF to factorize the joint form of magnitude and the absolute value of real and imaginary (RI) components that decompose the basis and weight matrices. Most researchers enhance the magnitude spectra only, ignore the phase spectra, and estimate the separated speech using noisy phase. As a result, some noise components are present in the estimated speech results. We are dealing with the signal's magnitude as well as the RI components and estimating the phase of the RI parts. Finally, separated speech signals can be achieved using the inverse STFT (ISTFT) and the inverse DDDTCWT (IDDDTCWT). Separation performance is improved for estimating the phase component and the shift-invariant, better direction selectivity, and scheme freedom properties of DDDTCWT. The speech separation efficiency of the proposed algorithm outperforms performance by 6.53–8.17 dB SDR gain, 7.37-9.87 dB SAR gain, and 14.92–17.21 dB SIR gain compared to the NMF method with masking on the TIMIT dataset.
语音是人类交流的基本要素，因此将语音与噪音区分开来至关重要。在现实世界中，由于背景噪声和语音重叠，语音分离变得极具挑战性。此外，使用短期傅里叶变换（STFT）和离散小波变换（DWT）进行语音分离时，需要分别解决时间和频率分辨率以及时变问题。为解决上述问题，本文提出了一种基于双密度双树复小波变换（DDDTCWT）和稀疏非负矩阵因式分解（SNMF）的新型语音分离技术。通过 DDDTCWT 小波分解，信号被分离成高通和低通频率分量。在本分析中，我们只考虑低通频率分量，而将高通频率分量清零。随后，STFT 应用于每个子带信号，生成复频谱图。因此，我们使用 SNMF 对分解基矩阵和权重矩阵的幅值和实部与虚部（RI）分量的绝对值的联合形式进行因式分解。大多数研究人员只增强了幅度频谱，忽略了相位频谱，并使用噪声相位来估计分离的语音。因此，在估计的语音结果中会出现一些噪声成分。我们既要处理信号的幅度，也要处理 RI 分量，并估算 RI 部分的相位。最后，可以使用反 STFT（ISTFT）和反 DDDTCWT（IDDTCWT）来分离语音信号。在估计相位分量和 DDDTCWT 的移位不变性、更好的方向选择性和方案自由度特性时，分离性能得到了提高。在 TIMIT 数据集上，与带掩码的 NMF 方法相比，所提算法的语音分离效率提高了 6.53-8.17 dB SDR 增益、7.37-9.87 dB SAR 增益和 14.92-17.21 dB SIR 增益。
{"title":"Single-channel Speech Separation Based on Double-density Dual-tree CWT and SNMF","authors":"Md. Imran Hossain, Md. Abdur Rahim, Md. Najmul Hossain","doi":"10.33166/aetic.2024.01.001","DOIUrl":"https://doi.org/10.33166/aetic.2024.01.001","url":null,"abstract":"Speech is essential to human communication; therefore, distinguishing it from noise is crucial. Speech separation becomes challenging in real-world circumstances with background noise and overlapping speech. Moreover, the speech separation using short-term Fourier transform (STFT) and discrete wavelet transform (DWT) addresses time and frequency resolution and time-variation issues, respectively. To solve the above issues, a new speech separation technique is presented based on the double-density dual-tree complex wavelet transform (DDDTCWT) and sparse non-negative matrix factorization (SNMF). The signal is separated into high-pass and low-pass frequency components using DDDTCWT wavelet decomposition. For this analysis, we only considered the low-pass frequency components and zeroed out the high-pass ones. Subsequently, the STFT is then applied to each sub-band signal to generate a complex spectrogram. Therefore, we have used SNMF to factorize the joint form of magnitude and the absolute value of real and imaginary (RI) components that decompose the basis and weight matrices. Most researchers enhance the magnitude spectra only, ignore the phase spectra, and estimate the separated speech using noisy phase. As a result, some noise components are present in the estimated speech results. We are dealing with the signal's magnitude as well as the RI components and estimating the phase of the RI parts. Finally, separated speech signals can be achieved using the inverse STFT (ISTFT) and the inverse DDDTCWT (IDDDTCWT). Separation performance is improved for estimating the phase component and the shift-invariant, better direction selectivity, and scheme freedom properties of DDDTCWT. The speech separation efficiency of the proposed algorithm outperforms performance by 6.53–8.17 dB SDR gain, 7.37-9.87 dB SAR gain, and 14.92–17.21 dB SIR gain compared to the NMF method with masking on the TIMIT dataset.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.33166/aetic.2024.01.004
Agboeze Jude, Jia Uddin
Software defect prediction is a critical task in software engineering that aims to identify and mitigate potential defects in software systems. In recent years, numerous techniques and approaches have been developed to improve the accuracy and efficiency of the defect prediction model. In this research paper, we proposed a comprehensive approach that addresses class imbalance by utilizing stratified splitting, explainable AI techniques, and a hybrid machine learning algorithm. To mitigate the impact of class imbalance, we employed stratified splitting during the training and evaluation phases. This method ensures that the class distribution is maintained in both the training and testing sets, enabling the model to learn from and generalize to the minority class examples effectively. Furthermore, we leveraged explainable AI methods, Lime and Shap, to enhance interpretability in the machine learning models. To improve prediction accuracy, we propose a hybrid machine learning algorithm that combines the strength of multiple models. This hybridization allows us to exploit the strength of each model, resulting in improved overall performance. The experiment is evaluated using the NASA-MD datasets. The result revealed that handling the class imbalanced data using stratify splitting approach achieves a better overall performance than the SMOTE approach in Software Defect Detection (SDD).
{"title":"Explainable Software Defects Classification Using SMOTE and Machine Learning","authors":"Agboeze Jude, Jia Uddin","doi":"10.33166/aetic.2024.01.004","DOIUrl":"https://doi.org/10.33166/aetic.2024.01.004","url":null,"abstract":"Software defect prediction is a critical task in software engineering that aims to identify and mitigate potential defects in software systems. In recent years, numerous techniques and approaches have been developed to improve the accuracy and efficiency of the defect prediction model. In this research paper, we proposed a comprehensive approach that addresses class imbalance by utilizing stratified splitting, explainable AI techniques, and a hybrid machine learning algorithm. To mitigate the impact of class imbalance, we employed stratified splitting during the training and evaluation phases. This method ensures that the class distribution is maintained in both the training and testing sets, enabling the model to learn from and generalize to the minority class examples effectively. Furthermore, we leveraged explainable AI methods, Lime and Shap, to enhance interpretability in the machine learning models. To improve prediction accuracy, we propose a hybrid machine learning algorithm that combines the strength of multiple models. This hybridization allows us to exploit the strength of each model, resulting in improved overall performance. The experiment is evaluated using the NASA-MD datasets. The result revealed that handling the class imbalanced data using stratify splitting approach achieves a better overall performance than the SMOTE approach in Software Defect Detection (SDD).","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"19 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.33166/aetic.2024.01.005
Sristy Shidul Nath, Razuan Karim, Mahdi H. Miraz
The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model’s accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.
{"title":"Deep Learning Based Cyberbullying Detection in Bangla Language","authors":"Sristy Shidul Nath, Razuan Karim, Mahdi H. Miraz","doi":"10.33166/aetic.2024.01.005","DOIUrl":"https://doi.org/10.33166/aetic.2024.01.005","url":null,"abstract":"The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model’s accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"17 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139129751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.33166/aetic.2024.01.002
Md. Shahriar Khan Hemel, M. Reaz, S. Ali, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz
The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps.
{"title":"Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices","authors":"Md. Shahriar Khan Hemel, M. Reaz, S. Ali, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz","doi":"10.33166/aetic.2024.01.002","DOIUrl":"https://doi.org/10.33166/aetic.2024.01.002","url":null,"abstract":"The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"99 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.33166/aetic.2024.01.003
Mahmoud A. A. Mousa, Abdelrahman Elgohr, Hatem A. Khater
The design of the robotic arm's trajectory is based on inverse kinematics problem solving, with additional refinements of certain criteria. One common design issue is the trajectory optimization of the robotic arm. Due to the difficulty of the work in the past, many of the suggested ways only resulted in a marginal improvement. This paper introduces two approaches to solve the problem of achieving robotic arm trajectory control while maintaining the minimum reachability time. These two approaches are based on rule-based optimization and a genetic algorithm. The way we addressed the problem here is based on the robot’s forward and inverse kinematics and takes into account the minimization of operating time throughout the operation cycle. The proposed techniques were validated, and all recommended criteria were compared on the trajectory optimization of the KUKA KR 4 R600 six-degree-of-freedom robot. As a conclusion, the genetic based algorithm behaves better than the rule-based one in terms of achieving a minimal trip time. We found that solutions generated by the Genetic based algorithm are approximately 3 times faster than the other solutions generated by the rule-based algorithm to the same paths. We argue that as the rule-based algorithm produces its solutions after discovering all the problem’s searching space which is time consuming, and it is not the case as per the genetic based algorithm.
{"title":"Trajectory Optimization for a 6 DOF Robotic Arm Based on Reachability Time","authors":"Mahmoud A. A. Mousa, Abdelrahman Elgohr, Hatem A. Khater","doi":"10.33166/aetic.2024.01.003","DOIUrl":"https://doi.org/10.33166/aetic.2024.01.003","url":null,"abstract":"The design of the robotic arm's trajectory is based on inverse kinematics problem solving, with additional refinements of certain criteria. One common design issue is the trajectory optimization of the robotic arm. Due to the difficulty of the work in the past, many of the suggested ways only resulted in a marginal improvement. This paper introduces two approaches to solve the problem of achieving robotic arm trajectory control while maintaining the minimum reachability time. These two approaches are based on rule-based optimization and a genetic algorithm. The way we addressed the problem here is based on the robot’s forward and inverse kinematics and takes into account the minimization of operating time throughout the operation cycle. The proposed techniques were validated, and all recommended criteria were compared on the trajectory optimization of the KUKA KR 4 R600 six-degree-of-freedom robot. As a conclusion, the genetic based algorithm behaves better than the rule-based one in terms of achieving a minimal trip time. We found that solutions generated by the Genetic based algorithm are approximately 3 times faster than the other solutions generated by the rule-based algorithm to the same paths. We argue that as the rule-based algorithm produces its solutions after discovering all the problem’s searching space which is time consuming, and it is not the case as per the genetic based algorithm.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139129227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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