Pub Date : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074273
K. Radha, R. Parameswari
Software-defined networking (SDN) is a networking architecture that enables simple programming of network devices. SDN allows the isolation of network resources using clearly defined APIs to accomplish multiple tenant networks with appropriate QoS (Quality of Service) and SLAs. The most critical issues among Cloud providers and organizational network infrastructures are Denial of Service (DDoS) attacks. SDN is more susceptible to controller resource exhaustion due to the rising frequency of distributed denial-of-service (DDoS) attacks. DDoS attacks make it difficult for the SDN controller to handle all received packets effectively, which may cause a network crash or deny authorized users access to network resources. The proposed traffic rate-based threshold (TRT) to overcome these DDoS attacks in SDN. The proposed work calculates the threshold based on the traffic and provides an alternate path enabling SDN with highly efficient and flexible solutions. To examine the efficiency of the proposed work, a comparison work is carried out with Renyi Joint Entropy-Based Dynamic Threshold Approach (RJE) and reactive & proactive (RP). The performance metrics considered are throughput, recovery time, and traffic. On all the metrics, the performance obtained by the proposed TRT is far better than the others.
{"title":"Reducing the Effects of DDos Attacks in Software Defined Networks Using Cloud Computing","authors":"K. Radha, R. Parameswari","doi":"10.1109/AICAPS57044.2023.10074273","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074273","url":null,"abstract":"Software-defined networking (SDN) is a networking architecture that enables simple programming of network devices. SDN allows the isolation of network resources using clearly defined APIs to accomplish multiple tenant networks with appropriate QoS (Quality of Service) and SLAs. The most critical issues among Cloud providers and organizational network infrastructures are Denial of Service (DDoS) attacks. SDN is more susceptible to controller resource exhaustion due to the rising frequency of distributed denial-of-service (DDoS) attacks. DDoS attacks make it difficult for the SDN controller to handle all received packets effectively, which may cause a network crash or deny authorized users access to network resources. The proposed traffic rate-based threshold (TRT) to overcome these DDoS attacks in SDN. The proposed work calculates the threshold based on the traffic and provides an alternate path enabling SDN with highly efficient and flexible solutions. To examine the efficiency of the proposed work, a comparison work is carried out with Renyi Joint Entropy-Based Dynamic Threshold Approach (RJE) and reactive & proactive (RP). The performance metrics considered are throughput, recovery time, and traffic. On all the metrics, the performance obtained by the proposed TRT is far better than the others.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122642670","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074510
Moumita Pal, R. V. Prasad
As www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it’s hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%).
{"title":"Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network & Supervised Approach","authors":"Moumita Pal, R. V. Prasad","doi":"10.1109/AICAPS57044.2023.10074510","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074510","url":null,"abstract":"As www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it’s hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%).","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115647702","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}
One of the main agendas behind any business is understanding their customer's views about their products. In the proposed model text analysis is performed on the feedback which is given on the VR model of a car customization application. This application is built in Unity with help of its XR toolkit. The text analysis is done using NLTK and Scikit learn. This text analysis will be based on multiple options provided by the VR model. This analysis will help automobile companies to know better about their customer's opinions. Customers will also be able to customize their cars with the help of the features provided in the application. Text analysis covers the summarization of the feedback form that is provided to the customers.
{"title":"VR for automobile customization and its feedback analysis","authors":"P. Ghadekar, Khushi Jhanwar, Ameya Karpe, Tanishka Shetty, Akash Sivanandan, Prannay Khushalani","doi":"10.1109/AICAPS57044.2023.10074233","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074233","url":null,"abstract":"One of the main agendas behind any business is understanding their customer's views about their products. In the proposed model text analysis is performed on the feedback which is given on the VR model of a car customization application. This application is built in Unity with help of its XR toolkit. The text analysis is done using NLTK and Scikit learn. This text analysis will be based on multiple options provided by the VR model. This analysis will help automobile companies to know better about their customer's opinions. Customers will also be able to customize their cars with the help of the features provided in the application. Text analysis covers the summarization of the feedback form that is provided to the customers.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123268404","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074415
Baazil P. Thampy, J. V., A. Kottayil
Wind profiler radars can continually and effectively probe the atmosphere to obtain the Doppler power spectrum of ambient air motion. In addition to ambient air motion, the Doppler power spectrum used to get wind estimates may contain atmospheric and non-atmospheric disturbances. The wind estimations might be biased as a result of these disruptions. Accurate detection of ambient air motion, even in the presence of disturbances, is essential to reduce the impact of these biases. The Doppler power spectrum can be segmented using cutting-edge deep learning models to retrieve ambient air motion. In this work, we used one of the finest deep learning models, U-net, to segment the Doppler power spectrum. The proposed method’s performance evaluation shows promising results in segmenting and retrieving ambient air motion.
{"title":"Wind profiler Doppler power spectrum segmentation using U-Net","authors":"Baazil P. Thampy, J. V., A. Kottayil","doi":"10.1109/AICAPS57044.2023.10074415","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074415","url":null,"abstract":"Wind profiler radars can continually and effectively probe the atmosphere to obtain the Doppler power spectrum of ambient air motion. In addition to ambient air motion, the Doppler power spectrum used to get wind estimates may contain atmospheric and non-atmospheric disturbances. The wind estimations might be biased as a result of these disruptions. Accurate detection of ambient air motion, even in the presence of disturbances, is essential to reduce the impact of these biases. The Doppler power spectrum can be segmented using cutting-edge deep learning models to retrieve ambient air motion. In this work, we used one of the finest deep learning models, U-net, to segment the Doppler power spectrum. The proposed method’s performance evaluation shows promising results in segmenting and retrieving ambient air motion.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122539574","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074386
S. S, Reham R. Mostafa, M. Bannany, A. Khedr
The emerging Internet of Things (IoT)-based Wireless Sensor Networks (WSN) consist of small size of sensor nodes for monitoring and collecting data from environmental conditions and it transmits to other sensors through the internet. The major issues in WSN are energy constraints that degrade the efficient functioning and lifetime of WSN. Therefore, a novel technique called Gradient Enhanced Broken-stick Regressive Multivariate Artificial Fish Swarm Optimized Data Collection (GEBRMAFSODC) is introduced. The main objective of the GEBRMAFSODC technique for performing energy-efficient data collection with lesser delay , data loss. Smart cities improve effectiveness of different applications including public transport services. By applying this method, the resource efficient optimal path and the population of artificial fishes (i.e. sensor nodes) is randomly initialized in the search space. For each node, fitness is measured depend on multivariate function namely energy, bandwidth, and distance. The Gradient Enhanced Broken-stick Regression is applied to fitness estimation for analyzing the resources and finding the optimal results. Efficient neighboring nodes are selected to transmit the collected data to sink node via best path. Sink node perform as a data collector with better resource sensor nodes through lesser delay. Simulation is conducted in NS2 simulator using Warrigal Dataset and the performance is analyzed by various parameters namely energy consumption, data collection delay, throughput, and data loss rate based on number of data. The observed result shows the superior performance of the proposed GEBRMAFSODC technique with a higher delivery ratio, throughput by 10%, 48% and lesser loss, delay, and energy consumption by 53%, 37%, and 27% as compared to other related methods respectively.
{"title":"Gradient Enhanced Regressive Multivariate Artificial Fish Swarm Optimized Data Collection for IoT-Enabled WSN in Smart Environments","authors":"S. S, Reham R. Mostafa, M. Bannany, A. Khedr","doi":"10.1109/AICAPS57044.2023.10074386","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074386","url":null,"abstract":"The emerging Internet of Things (IoT)-based Wireless Sensor Networks (WSN) consist of small size of sensor nodes for monitoring and collecting data from environmental conditions and it transmits to other sensors through the internet. The major issues in WSN are energy constraints that degrade the efficient functioning and lifetime of WSN. Therefore, a novel technique called Gradient Enhanced Broken-stick Regressive Multivariate Artificial Fish Swarm Optimized Data Collection (GEBRMAFSODC) is introduced. The main objective of the GEBRMAFSODC technique for performing energy-efficient data collection with lesser delay , data loss. Smart cities improve effectiveness of different applications including public transport services. By applying this method, the resource efficient optimal path and the population of artificial fishes (i.e. sensor nodes) is randomly initialized in the search space. For each node, fitness is measured depend on multivariate function namely energy, bandwidth, and distance. The Gradient Enhanced Broken-stick Regression is applied to fitness estimation for analyzing the resources and finding the optimal results. Efficient neighboring nodes are selected to transmit the collected data to sink node via best path. Sink node perform as a data collector with better resource sensor nodes through lesser delay. Simulation is conducted in NS2 simulator using Warrigal Dataset and the performance is analyzed by various parameters namely energy consumption, data collection delay, throughput, and data loss rate based on number of data. The observed result shows the superior performance of the proposed GEBRMAFSODC technique with a higher delivery ratio, throughput by 10%, 48% and lesser loss, delay, and energy consumption by 53%, 37%, and 27% as compared to other related methods respectively.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130157692","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074588
Anandhi V, V. P, Varun G. Menon, Abhijith Krishna E R, Akshay Shilesh, Akshay Viswam, Amin Shafiq
Malware detection is an indispensable factor in the security of internet-oriented machines. The number of threats have been increased day by day. Malware analysis is a process of performing analysis and a study of the components and behavior of malware. The use of dynamic analysis will help the system to classify malware more accurately and to detect any malware samples. Dynamic analysis is a method in which the malware runs in a Sandbox environment, and artifacts are collected. The system uses Cuckoo Sandbox for executing the malware samples in a controlled environment. The system compares bidirectional long short-term memory and convolutional neural network models for machine learning algorithms to detect and classify the malware samples. Unlike a typical signature-based detection, where patterns are checked in the source file, a type of static detection, here, dynamic analysis is used to extract necessary reports, which are then preprocessed to get features like dynamic link library (dlls), kernel module names, services used, etc. to try creating a list of text, which can explain the behaviour of the executable file. These are tokenized and embedded to obtain numerical data, which is passed to the models. The accuracy of trained models is compared, which describes the performance of the models on the dataset. Thus providing grounds for testing future models and later building a better detection system based on it.
{"title":"Malware Detection using Dynamic Analysis","authors":"Anandhi V, V. P, Varun G. Menon, Abhijith Krishna E R, Akshay Shilesh, Akshay Viswam, Amin Shafiq","doi":"10.1109/AICAPS57044.2023.10074588","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074588","url":null,"abstract":"Malware detection is an indispensable factor in the security of internet-oriented machines. The number of threats have been increased day by day. Malware analysis is a process of performing analysis and a study of the components and behavior of malware. The use of dynamic analysis will help the system to classify malware more accurately and to detect any malware samples. Dynamic analysis is a method in which the malware runs in a Sandbox environment, and artifacts are collected. The system uses Cuckoo Sandbox for executing the malware samples in a controlled environment. The system compares bidirectional long short-term memory and convolutional neural network models for machine learning algorithms to detect and classify the malware samples. Unlike a typical signature-based detection, where patterns are checked in the source file, a type of static detection, here, dynamic analysis is used to extract necessary reports, which are then preprocessed to get features like dynamic link library (dlls), kernel module names, services used, etc. to try creating a list of text, which can explain the behaviour of the executable file. These are tokenized and embedded to obtain numerical data, which is passed to the models. The accuracy of trained models is compared, which describes the performance of the models on the dataset. Thus providing grounds for testing future models and later building a better detection system based on it.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121550205","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074005
Varun Rishwandh Sekar, Thuhin Khanna Rajesh Kannan, Suraj N, P. Vijay
Natural Language Processing (NLP) is the branch of Artificial Intelligence that deals with the interpretation of human speech. NLP is a vast area of study that is continually being developed each day, with active research happening worldwide. The development of NLP algorithms is of utmost importance to the advancements in the field of Artificial Intelligence. With the increasing popularity of social media and the number of hours spent on social media multiplying, people share their opinion on a wide range of topics and issues. This sudden burst of data generated by social media platforms contains a massive potential when combined with state-of-the-art NLP models, it can be leveraged to our advantage. This work builds a dataset of user comments on the top posts about political leaders on Reddit, using Reddit’s API. On Reddit, extensive discussions on various topics occur daily. The end goal of this work is to rank the chosen world leaders based on their likability. To find the general likability of a public personality, we try to classify the comments collected from Reddit using Sentiment Analysis. This paper employs state-of-the-art NLP algorithms such as Flair, DistilBERT, and Text Blob Analysis, and combines the results to get a better final rank of the world leaders.
{"title":"Hybrid Perception Analysis of World Leaders in Reddit using Sentiment Analysis","authors":"Varun Rishwandh Sekar, Thuhin Khanna Rajesh Kannan, Suraj N, P. Vijay","doi":"10.1109/AICAPS57044.2023.10074005","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074005","url":null,"abstract":"Natural Language Processing (NLP) is the branch of Artificial Intelligence that deals with the interpretation of human speech. NLP is a vast area of study that is continually being developed each day, with active research happening worldwide. The development of NLP algorithms is of utmost importance to the advancements in the field of Artificial Intelligence. With the increasing popularity of social media and the number of hours spent on social media multiplying, people share their opinion on a wide range of topics and issues. This sudden burst of data generated by social media platforms contains a massive potential when combined with state-of-the-art NLP models, it can be leveraged to our advantage. This work builds a dataset of user comments on the top posts about political leaders on Reddit, using Reddit’s API. On Reddit, extensive discussions on various topics occur daily. The end goal of this work is to rank the chosen world leaders based on their likability. To find the general likability of a public personality, we try to classify the comments collected from Reddit using Sentiment Analysis. This paper employs state-of-the-art NLP algorithms such as Flair, DistilBERT, and Text Blob Analysis, and combines the results to get a better final rank of the world leaders.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132664323","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074347
Avantika Gaur, Arjun Singh, Aditya Nautiyal, Gaurav Kothari, P. Mishra, Aman Jha
Security holds great significance in this new era of on-demand virtual computing. As software and hardware update daily, malware is also modifying its behavior rapidly. Some researchers are still working in this area to handle the recent cyber-attacks in critical virtualization ecosystems. The existing research works may not be suitable with the existing updated virtualization environment as they have been validated with older datasets. In this paper, a deep neural network (DNN) based malware detection approach has been proposed, called DeepHyperv, to detect the malware threats in a virtualization environment by doing the deep virtual memory analysis. Direct access to the analysis components is prohibited in the proposed architecture by deploying them inside the privileged domain of the hypervisor. The process execution logs are collected at the hypervisor using the memory introspection technique with the support of recent hardware and software configurations of analysis setup and virtualization environment. The logs are pre-processed and converted into a discrete feature vector matrix. The approach uses DNN to learn & test the extracted features at the hypervisor. The approach is validated in the test bed setup of our lab, and results seem to promising.
{"title":"DeepHyperv: A deep neural network based virtual memory analysis for malware detection at hypervisor-layer","authors":"Avantika Gaur, Arjun Singh, Aditya Nautiyal, Gaurav Kothari, P. Mishra, Aman Jha","doi":"10.1109/AICAPS57044.2023.10074347","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074347","url":null,"abstract":"Security holds great significance in this new era of on-demand virtual computing. As software and hardware update daily, malware is also modifying its behavior rapidly. Some researchers are still working in this area to handle the recent cyber-attacks in critical virtualization ecosystems. The existing research works may not be suitable with the existing updated virtualization environment as they have been validated with older datasets. In this paper, a deep neural network (DNN) based malware detection approach has been proposed, called DeepHyperv, to detect the malware threats in a virtualization environment by doing the deep virtual memory analysis. Direct access to the analysis components is prohibited in the proposed architecture by deploying them inside the privileged domain of the hypervisor. The process execution logs are collected at the hypervisor using the memory introspection technique with the support of recent hardware and software configurations of analysis setup and virtualization environment. The logs are pre-processed and converted into a discrete feature vector matrix. The approach uses DNN to learn & test the extracted features at the hypervisor. The approach is validated in the test bed setup of our lab, and results seem to promising.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129010967","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074379
Walid Osamy, A. Salim, Amel Al Ali, A. Khedr
In this paper, a clustering approach called CATRSO is proposed. The selection of cluster heads (CH) is performed by considering the trust value of the nodes in order to select the most trustworthy nodes as CH and Rat Swarm Optimizer is employed for CH selection process. The trust value of the nodes and remaining energy are taken into account while designing the fitness function. In addition, a chain routing approach is employed between CHs for energy savings. The results demonstrate that the CATRSO technique is successful in selecting the most trustworthy nodes as CH and outperforms earlier efforts in the literature in terms of energy efficiency, average network lifetime, and trustworthiness of selected CHs.
{"title":"A New Clustering Approach based on Trust and Rat Swarm Algorithm for WSN Applications","authors":"Walid Osamy, A. Salim, Amel Al Ali, A. Khedr","doi":"10.1109/AICAPS57044.2023.10074379","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074379","url":null,"abstract":"In this paper, a clustering approach called CATRSO is proposed. The selection of cluster heads (CH) is performed by considering the trust value of the nodes in order to select the most trustworthy nodes as CH and Rat Swarm Optimizer is employed for CH selection process. The trust value of the nodes and remaining energy are taken into account while designing the fitness function. In addition, a chain routing approach is employed between CHs for energy savings. The results demonstrate that the CATRSO technique is successful in selecting the most trustworthy nodes as CH and outperforms earlier efforts in the literature in terms of energy efficiency, average network lifetime, and trustworthiness of selected CHs.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124865308","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 : 2023-02-01DOI: 10.1109/AICAPS57044.2023.10074495
Chellaswamy C, G. S, Ramasubramanian B, Dhelipan Raj A, Dhilipkumar S, Koushikkaran K
River water plays an important role in every metropolitan society; it contributes significantly to the production of agricultural products and hence to the economy of a country besides offering many other benefits. Therefore, river water monitoring is necessary though difficult. The goal of this research is to create a quantitative technique for assessing the water quality state of the Indian rivers in the southern part of India. Water test samples were obtained at three distinct places along the Kaveri River for this study. The water level information was retrieved from the photos using a hybrid method (a combination of convolutional neural network and long short-term memory network) called CNN-LSMN. The level points were measured using the field camera placed in the test locations. The following six typical metrics were used to assess the water quality: turbidity, temperature, pH, TDS, conductivity, and total hardness. In this study, the water quality index (WQI) of the modified National Sanitation Foundation (NSF) was used to determine the quality of water. Furthermore, the flower pollination optimization method was used to optimise the critical water quality indicators. Standard performance metrics were used to compare the performance of the proposed approach with that of the existing techniques. Upon comparing the performance of the suggested CNN-LSMN in terms of performance measures, it was found that the detection accuracy had improved and it was 4.62%. The proposed technique in this study was found to be beneficial for precisely estimating the water level and quality of the rivers.
{"title":"Smart River Water Quality and Level Monitoring: a Hybrid Neural Network Approach","authors":"Chellaswamy C, G. S, Ramasubramanian B, Dhelipan Raj A, Dhilipkumar S, Koushikkaran K","doi":"10.1109/AICAPS57044.2023.10074495","DOIUrl":"https://doi.org/10.1109/AICAPS57044.2023.10074495","url":null,"abstract":"River water plays an important role in every metropolitan society; it contributes significantly to the production of agricultural products and hence to the economy of a country besides offering many other benefits. Therefore, river water monitoring is necessary though difficult. The goal of this research is to create a quantitative technique for assessing the water quality state of the Indian rivers in the southern part of India. Water test samples were obtained at three distinct places along the Kaveri River for this study. The water level information was retrieved from the photos using a hybrid method (a combination of convolutional neural network and long short-term memory network) called CNN-LSMN. The level points were measured using the field camera placed in the test locations. The following six typical metrics were used to assess the water quality: turbidity, temperature, pH, TDS, conductivity, and total hardness. In this study, the water quality index (WQI) of the modified National Sanitation Foundation (NSF) was used to determine the quality of water. Furthermore, the flower pollination optimization method was used to optimise the critical water quality indicators. Standard performance metrics were used to compare the performance of the proposed approach with that of the existing techniques. Upon comparing the performance of the suggested CNN-LSMN in terms of performance measures, it was found that the detection accuracy had improved and it was 4.62%. The proposed technique in this study was found to be beneficial for precisely estimating the water level and quality of the rivers.","PeriodicalId":146698,"journal":{"name":"2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122953041","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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