Inam Ul Haq, Amna Nadeem, Saba Ramzan, Nazir Ahmad, Yasir Ahmad
: Due to inherent vulnerability, wireless networks require additional security, integrity, and authentication. The purpose of this study is to highlight the outdated ”Counter Mode Cipher Block Chaining Message Authentication Code Protocol” (CCMP) that has lately taken the place of the flawed ”Wired Equivalent Privacy” (WEP) protocol for authenticating IEEE 802.11 (WLANs). The IEEE 802.11s, a draught widespread for wireless networks in a mesh topology, also recommended using CCMP (WMNs). Due to CCMP’s two-pass operation, multi-hop wireless networks like WMN have a considerable latency problem. An increase in latency results in a decrease in service quality for multimedia applications as it is sensitive to delays. In addition to highlighting the CCMP’s vulnerability to pre-computation time-memory trade-o ff (TMTO) attacks, this paper recommends improving WLAN packet security by implementing a packet-by-packet security mechanism. Furthermore, we propose a fresh, dependable, low-latency foundation for WMN. Our security framework architecture employs a piggyback challenge-response mechanism to ensure data secrecy and data integrity. The use of a secret nonce, a new encryption key for each packet, and packet-level authentication are all features of the Piggyback challenge-response protocol. By authenticating every packet, unauthorized access can be swiftly prevented.
{"title":"Towards Robust and Low Latency Security Framework for\u0000IEEE 802.11 Wireless Networks","authors":"Inam Ul Haq, Amna Nadeem, Saba Ramzan, Nazir Ahmad, Yasir Ahmad","doi":"10.12785/ijcds/1501112","DOIUrl":"https://doi.org/10.12785/ijcds/1501112","url":null,"abstract":": Due to inherent vulnerability, wireless networks require additional security, integrity, and authentication. The purpose of this study is to highlight the outdated ”Counter Mode Cipher Block Chaining Message Authentication Code Protocol” (CCMP) that has lately taken the place of the flawed ”Wired Equivalent Privacy” (WEP) protocol for authenticating IEEE 802.11 (WLANs). The IEEE 802.11s, a draught widespread for wireless networks in a mesh topology, also recommended using CCMP (WMNs). Due to CCMP’s two-pass operation, multi-hop wireless networks like WMN have a considerable latency problem. An increase in latency results in a decrease in service quality for multimedia applications as it is sensitive to delays. In addition to highlighting the CCMP’s vulnerability to pre-computation time-memory trade-o ff (TMTO) attacks, this paper recommends improving WLAN packet security by implementing a packet-by-packet security mechanism. Furthermore, we propose a fresh, dependable, low-latency foundation for WMN. Our security framework architecture employs a piggyback challenge-response mechanism to ensure data secrecy and data integrity. The use of a secret nonce, a new encryption key for each packet, and packet-level authentication are all features of the Piggyback challenge-response protocol. By authenticating every packet, unauthorized access can be swiftly prevented.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"39 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735181","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}
{"title":"Semi-symmetrical Coprime Linear Array with Reduced\u0000Mutual Coupling Effect and High Degrees of Freedom","authors":"Fatimah Abdulnabi Salman, Bayan Mahdi Sabbar","doi":"10.12785/ijcds/1501105","DOIUrl":"https://doi.org/10.12785/ijcds/1501105","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353165","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}
{"title":"Deep Learning Model For Autism Diagnosing","authors":"Mazin R. Swadi, Muayad S. Croock","doi":"10.12785/ijcds/1501109","DOIUrl":"https://doi.org/10.12785/ijcds/1501109","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357068","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}
: The need for cloud computing has increased in the age of contemporary networked systems, driving the pursuit of optimal resource allocation and data processing. It is imperative in essential fields where security, such as transportation systems, depends on computing performance. Even after much research has been done on managing resources in cloud computing, finding algorithms that maximize job completion, minimize costs, and maximize resource consumption has remained a top priority. However, existing techniques have shown limitations, which calls for new ways. Our work shows the novel hybrid approach that has the potential to change the game completely. The Neural Network Task Classification (N2TC) is the result of merging neural networks with genetic algorithms. This ground-breaking method skillfully applies the Genetic Algorithm Task Assignment (GATA) for resource allocation while utilizing neural networks for task categorization. Notably, our algorithm carefully considers execution time, response time, costs, and system e ffi ciency to promote fairness, a defense against resource scarcity. Our method achieves a remarkable 13.3% cost reduction, a stunning 12.1% increase in response time, and a 3.2% increase in execution time. These strong indicators act as a wake-up call, announcing our hybrid algorithm’s power and revolutionary potential in transforming the paradigms around cloud-based task scheduling. This work represents a turning point in cloud computing, demonstrating an innovative combination of algorithms that not only overcomes current constraints but also ushers in a new era of e ffi cacy and e ffi ciency with far-reaching implications outside the domain of transportation systems
{"title":"Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid\u0000Algorithm for Optimal Resource Allocation and Efficiency in\u0000Contemporary Networked Systems","authors":"Punit Mittal, Satender Kumar, Swati Sharma","doi":"10.12785/ijcds/1501110","DOIUrl":"https://doi.org/10.12785/ijcds/1501110","url":null,"abstract":": The need for cloud computing has increased in the age of contemporary networked systems, driving the pursuit of optimal resource allocation and data processing. It is imperative in essential fields where security, such as transportation systems, depends on computing performance. Even after much research has been done on managing resources in cloud computing, finding algorithms that maximize job completion, minimize costs, and maximize resource consumption has remained a top priority. However, existing techniques have shown limitations, which calls for new ways. Our work shows the novel hybrid approach that has the potential to change the game completely. The Neural Network Task Classification (N2TC) is the result of merging neural networks with genetic algorithms. This ground-breaking method skillfully applies the Genetic Algorithm Task Assignment (GATA) for resource allocation while utilizing neural networks for task categorization. Notably, our algorithm carefully considers execution time, response time, costs, and system e ffi ciency to promote fairness, a defense against resource scarcity. Our method achieves a remarkable 13.3% cost reduction, a stunning 12.1% increase in response time, and a 3.2% increase in execution time. These strong indicators act as a wake-up call, announcing our hybrid algorithm’s power and revolutionary potential in transforming the paradigms around cloud-based task scheduling. This work represents a turning point in cloud computing, demonstrating an innovative combination of algorithms that not only overcomes current constraints but also ushers in a new era of e ffi cacy and e ffi ciency with far-reaching implications outside the domain of transportation systems","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140355069","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}
Dawla Almulla, Mohammed Abbas, Adel Al-Alawi, Lamya Alkooheji
{"title":"Process and Impact Evaluation of Artificial Intelligence in\u0000Managerial Accounting: A Systematic Literature Review","authors":"Dawla Almulla, Mohammed Abbas, Adel Al-Alawi, Lamya Alkooheji","doi":"10.12785/ijcds/1501104","DOIUrl":"https://doi.org/10.12785/ijcds/1501104","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353246","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}
Sudeshna Sani, Samudra Vijaya, Suryakanth V Gangashetty
: Since 1991, machine translation has been a prominent research area in India, with IIT Kanpur pioneering the original work which has since been expanded to several universities. Only 10 percent of India’s 1.3 billion inhabitants can read, write, and speak English with varying degrees of competence, which makes machine translation crucial in overcoming the linguistic barrier to the internet. The Indian market for commercial products and events is greatly influenced by local languages, making the development and translation of region-based content an essential research topic nowadays. However, Indic-to-Indic language direct translation has faced several challenges and is still going through the experimental phase. Several government-sponsored projects are being undertaken in this regard. Still, there are limited sentence-aligned parallel bi-text resources available for the majority of Indian language pairs. This paper presents a detailed survey of the current trends of research on machine translation between Indian languages, along with their challenges over time. It also presents a timeline of recent research conducted and key findings of past surveys conducted over a decade. Under a single canopy, this paper provides sources of data, the progress made in developing datasets for low-resource Indian languages, various models of translation, encouragement from Indian Govt., and finally, new research directions.
{"title":"A Survey on the MT Methods for Indian Languages: MT\u0000Challenges, Availability, and Production of Parallel Corpora,\u0000Government Policies and Research Directions","authors":"Sudeshna Sani, Samudra Vijaya, Suryakanth V Gangashetty","doi":"10.12785/ijcds/1501107","DOIUrl":"https://doi.org/10.12785/ijcds/1501107","url":null,"abstract":": Since 1991, machine translation has been a prominent research area in India, with IIT Kanpur pioneering the original work which has since been expanded to several universities. Only 10 percent of India’s 1.3 billion inhabitants can read, write, and speak English with varying degrees of competence, which makes machine translation crucial in overcoming the linguistic barrier to the internet. The Indian market for commercial products and events is greatly influenced by local languages, making the development and translation of region-based content an essential research topic nowadays. However, Indic-to-Indic language direct translation has faced several challenges and is still going through the experimental phase. Several government-sponsored projects are being undertaken in this regard. Still, there are limited sentence-aligned parallel bi-text resources available for the majority of Indian language pairs. This paper presents a detailed survey of the current trends of research on machine translation between Indian languages, along with their challenges over time. It also presents a timeline of recent research conducted and key findings of past surveys conducted over a decade. Under a single canopy, this paper provides sources of data, the progress made in developing datasets for low-resource Indian languages, various models of translation, encouragement from Indian Govt., and finally, new research directions.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353754","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}
: Over the last few decades, there has been a significant increase in the availability and utilization of large music collections. However, most studies of these collections have been limited to Western music, which hinders our ability to comprehend the diversity and commonality of music across all cultures. Based on popularity from Spotify, it has been discovered that Bangladeshi rock music is more popular than the rock music from West Bengal, India. Previous research suggests that listeners from diverse cultural backgrounds may have varying preferences when comes to music appreciation. This research aimed to explore the reasons behind the popularity of Bangladeshi rock music compared to West Bengal rock music. By extracting various features from songs, the study sought to identify what is the reason behind the popularity of Bangladeshi rock music and whether there are any di ff erences in terms of musical features.
{"title":"Rocking Across Borders : An Analysis of the Musical\u0000Differences between Bangladesh and West-Bengal Rock Songs\u0000Using Spotify Audio Features","authors":"Moshiur Rahman Autul, Durjoy Dey, Partha Protim Paul, Mohammed Raihan Ullah","doi":"10.12785/ijcds/1501106","DOIUrl":"https://doi.org/10.12785/ijcds/1501106","url":null,"abstract":": Over the last few decades, there has been a significant increase in the availability and utilization of large music collections. However, most studies of these collections have been limited to Western music, which hinders our ability to comprehend the diversity and commonality of music across all cultures. Based on popularity from Spotify, it has been discovered that Bangladeshi rock music is more popular than the rock music from West Bengal, India. Previous research suggests that listeners from diverse cultural backgrounds may have varying preferences when comes to music appreciation. This research aimed to explore the reasons behind the popularity of Bangladeshi rock music compared to West Bengal rock music. By extracting various features from songs, the study sought to identify what is the reason behind the popularity of Bangladeshi rock music and whether there are any di ff erences in terms of musical features.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"9 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353941","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}
Suha Abdulhussein Abdulzahra, Ali Kadhum M. Al-Qurabat
: UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ine ff ective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.
{"title":"Exploring Radio Frequency-Based UAV Localization\u0000Techniques: A Comprehensive Review","authors":"Suha Abdulhussein Abdulzahra, Ali Kadhum M. Al-Qurabat","doi":"10.12785/ijcds/1501111","DOIUrl":"https://doi.org/10.12785/ijcds/1501111","url":null,"abstract":": UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ine ff ective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140355318","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}
: The significance of face recognition technology spans across diverse domains due to its practical applications. This study introduces an innovative face recognition system that seamlessly integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for precise face detection, VGGFace for feature extraction, and Support Vector Machine (SVM) for e ffi cient classification. The system demonstrates exceptional real-time performance in tracking multiple faces within a single frame, particularly excelling in attendance monitoring. Notably, the ”VGGFace” model emerges as a standout performer, showcasing remarkable accuracy and achieving an impressive F-score of 95% when coupled with SVM. This underscores the model’s e ff ectiveness in recognizing facial identities, attributing its success to robust training on extensive datasets. The research underscores the potency of the VGGFace model, especially in collaboration with various classifiers, with SVM yielding notably high accuracy rates.
{"title":"Attendance System Optimization through Deep Learning Face\u0000Recognition","authors":"Mahmoud Ali, Anjali Diwan, Dinesh Kumar","doi":"10.12785/ijcds/1501108","DOIUrl":"https://doi.org/10.12785/ijcds/1501108","url":null,"abstract":": The significance of face recognition technology spans across diverse domains due to its practical applications. This study introduces an innovative face recognition system that seamlessly integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for precise face detection, VGGFace for feature extraction, and Support Vector Machine (SVM) for e ffi cient classification. The system demonstrates exceptional real-time performance in tracking multiple faces within a single frame, particularly excelling in attendance monitoring. Notably, the ”VGGFace” model emerges as a standout performer, showcasing remarkable accuracy and achieving an impressive F-score of 95% when coupled with SVM. This underscores the model’s e ff ectiveness in recognizing facial identities, attributing its success to robust training on extensive datasets. The research underscores the potency of the VGGFace model, especially in collaboration with various classifiers, with SVM yielding notably high accuracy rates.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"9 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140352836","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}
Ahmed Badawi, Hassan Ali, I. M. Elzein, Alhareth M. Zyoud
{"title":"Performance Evaluation of Incremental Conductance and\u0000Adaptive HCS MPPT Algorithms for WECS","authors":"Ahmed Badawi, Hassan Ali, I. M. Elzein, Alhareth M. Zyoud","doi":"10.12785/ijcds/1501101","DOIUrl":"https://doi.org/10.12785/ijcds/1501101","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"57 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238493","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：