Pub Date : 2023-11-01DOI: 10.3844/jcssp.2023.1369.1379
Dedy Rahman Wijaya, Aqil Athallah, Tiara Nuha Noor’afina, Patrick Adolf Telnoni, Sari Dewi Budiwati
The Indonesian logistics industry is facing significant challenges related to inefficiency and irregularity, particularly in the commodity cargo route system. This issue is further exacerbated by the high logistics costs in the country, which currently stands at 23%, higher than that of other countries. To address this issue, this study proposes an implementation and examination of several algorithms Greedy, Best First Search (BFS), Dijkstra’s, A*, and Floyd-Warshall to optimize the cargo route system and reduce logistics costs. The algorithms were compared using various parameters, including price, distance, rating, and time. The results revealed that the Greedy algorithm is not a reliable option for cargo route optimization. In contrast, the A* algorithm offered the best solution compared to other algorithms, although it was not the fastest in terms of time. This study emphasizes the importance of considering various factors to optimize the cargo route system effectively. The experiments conducted in this study offer promising insights and pave the way for further research to improve the efficiency and reliability of the logistics industry in Indonesia.
{"title":"Cargo Route Optimization Using Shortest Path Algorithms: Runtime and Validity Comparison","authors":"Dedy Rahman Wijaya, Aqil Athallah, Tiara Nuha Noor’afina, Patrick Adolf Telnoni, Sari Dewi Budiwati","doi":"10.3844/jcssp.2023.1369.1379","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1369.1379","url":null,"abstract":"The Indonesian logistics industry is facing significant challenges related to inefficiency and irregularity, particularly in the commodity cargo route system. This issue is further exacerbated by the high logistics costs in the country, which currently stands at 23%, higher than that of other countries. To address this issue, this study proposes an implementation and examination of several algorithms Greedy, Best First Search (BFS), Dijkstra’s, A*, and Floyd-Warshall to optimize the cargo route system and reduce logistics costs. The algorithms were compared using various parameters, including price, distance, rating, and time. The results revealed that the Greedy algorithm is not a reliable option for cargo route optimization. In contrast, the A* algorithm offered the best solution compared to other algorithms, although it was not the fastest in terms of time. This study emphasizes the importance of considering various factors to optimize the cargo route system effectively. The experiments conducted in this study offer promising insights and pave the way for further research to improve the efficiency and reliability of the logistics industry in Indonesia.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112363","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 : 2023-11-01DOI: 10.3844/jcssp.2023.1398.1409
Bruno Bevilaqua, Marco Aurélio Spohn
The Message Queuing Telemetry Transport (MQTT) protocol is most used in Internet of Things (IoT) applications. The protocol implements the Publish/Subscribe (P/S) communication model. Publishers are entities providing data to a server (broker), and subscribers are those showing interest in such data. The standard MQTT scenario relies on a single broker, a potential bottleneck, and a single point of failure. The best way to scale MQTT systems is through horizontal approaches like clustering and federation. In particular, this study focuses on improving the capabilities of a self-managed federation of brokers. We present the first solution to address the dynamic management of an overlay network for the federation of autonomous brokers. The system provides the primary mechanisms for building and self-healing the federation network. We develop a new variant for the original federation protocol integrating the dynamic topology management. We present a case study as a proof of concept, showing that all designed features work as expected.
{"title":"Self-Managed Federation of MQTT Brokers with Dynamic Topology Control","authors":"Bruno Bevilaqua, Marco Aurélio Spohn","doi":"10.3844/jcssp.2023.1398.1409","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1398.1409","url":null,"abstract":"The Message Queuing Telemetry Transport (MQTT) protocol is most used in Internet of Things (IoT) applications. The protocol implements the Publish/Subscribe (P/S) communication model. Publishers are entities providing data to a server (broker), and subscribers are those showing interest in such data. The standard MQTT scenario relies on a single broker, a potential bottleneck, and a single point of failure. The best way to scale MQTT systems is through horizontal approaches like clustering and federation. In particular, this study focuses on improving the capabilities of a self-managed federation of brokers. We present the first solution to address the dynamic management of an overlay network for the federation of autonomous brokers. The system provides the primary mechanisms for building and self-healing the federation network. We develop a new variant for the original federation protocol integrating the dynamic topology management. We present a case study as a proof of concept, showing that all designed features work as expected.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111398","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 : 2023-10-01DOI: 10.3844/jcssp.2023.1231.1241
Yagnasree Sirivella, Anuj Jain
Recent advances in technology are very astounding since they have made it possible to manage and monitor systems remotely. Traditional farming is undergoing a transition towards "smart farming" with the assistance of technological breakthroughs, which include the implementation of intelligent irrigation systems and the remote monitoring of the development of crops. In particular, the Unmanned Aerial Vehicle plays a significant role in sophisticated UAVs' ability to capture photographs of crops and spray for pests. The image that is obtained from UAVs is then subjected to various forms of computer-assisted processing in order to determine whether or not the crop's leaves are naturally healthy, diseased, or rotten. Several groups of researchers investigated a variety of approaches, including clustering, machine learning, and deep learning, with the goal of determining the nature of the leaves and categorizing them according to the characteristics they possessed. These traits are necessary for categorization, but the time required to process them will be increased because of their enormous size. Because of this, the authors of this study present a hybrid feature reduction technique that is a blend of two different metaheuristic algorithms. In this case, an upgraded version of the cuckoo search algorithm was paired with the particle swarm to find the most advantageous characteristics. In this, the optimum features of the texture, such as its GLCM, GLDM, and local binary pattern features, were chosen for selection. Using a neural network that was based on the back propagation technique, the optimal characteristics were used for classification. The method that has been suggested is based on photographs that were taken in natural settings of sets of healthy and diseased leaf specimens. The entire process is carried out with the assistance of the MATLAB R2021a program and the results are analyzed with Accuracy, Sensitivity, and Specificity.
{"title":"A Hybrid Metaheuristic Algorithm for Diseases Classification Using UAV Images","authors":"Yagnasree Sirivella, Anuj Jain","doi":"10.3844/jcssp.2023.1231.1241","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1231.1241","url":null,"abstract":"Recent advances in technology are very astounding since they have made it possible to manage and monitor systems remotely. Traditional farming is undergoing a transition towards \"smart farming\" with the assistance of technological breakthroughs, which include the implementation of intelligent irrigation systems and the remote monitoring of the development of crops. In particular, the Unmanned Aerial Vehicle plays a significant role in sophisticated UAVs' ability to capture photographs of crops and spray for pests. The image that is obtained from UAVs is then subjected to various forms of computer-assisted processing in order to determine whether or not the crop's leaves are naturally healthy, diseased, or rotten. Several groups of researchers investigated a variety of approaches, including clustering, machine learning, and deep learning, with the goal of determining the nature of the leaves and categorizing them according to the characteristics they possessed. These traits are necessary for categorization, but the time required to process them will be increased because of their enormous size. Because of this, the authors of this study present a hybrid feature reduction technique that is a blend of two different metaheuristic algorithms. In this case, an upgraded version of the cuckoo search algorithm was paired with the particle swarm to find the most advantageous characteristics. In this, the optimum features of the texture, such as its GLCM, GLDM, and local binary pattern features, were chosen for selection. Using a neural network that was based on the back propagation technique, the optimal characteristics were used for classification. The method that has been suggested is based on photographs that were taken in natural settings of sets of healthy and diseased leaf specimens. The entire process is carried out with the assistance of the MATLAB R2021a program and the results are analyzed with Accuracy, Sensitivity, and Specificity.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922314","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 : 2023-10-01DOI: 10.3844/jcssp.2023.1222.1230
Muhammad Amien Ibrahim, Samsul Arifin, Eko Setyo Purwanto
Social media moderation is a crucial component to establish healthy online communities and ensuring online safety from hate speech and offensive language. In many cases, hate speech may be targeted at specific gender which could be expressed in many different languages on social media platforms such as Indonesian Twitter. However, difficulties such as data scarcity and the imbalanced gender-based hate speech dataset in Indonesian tweets have slowed the development and implementation of automatic social media moderation. Obtaining more data to increase the number of samples may be costly in terms of resources required to gather and annotate the data. This study looks at the usage of data augmentation methods to increase the amount of textual dataset while keeping the quality of the augmented data. Three augmentation strategies are explored in this study: Random insertion, back translation, and a sequential combination of back translation and random insertion. Additionally, the study examines the preservation of the increased data labels. The performance result demonstrates that classification models trained with augmented data generated from random insertion strategy outperform the other approaches. In terms of label preservation, the three augmentation approaches have been shown to offer enough label preservation without compromising the meaning of the augmented data. The findings imply that by increasing the amount of the dataset while preserving the original label, data augmentation could be utilized to solve issues such as data scarcity and dataset imbalance.
{"title":"Exploring Data Augmentation for Gender-Based Hate Speech Detection","authors":"Muhammad Amien Ibrahim, Samsul Arifin, Eko Setyo Purwanto","doi":"10.3844/jcssp.2023.1222.1230","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1222.1230","url":null,"abstract":"Social media moderation is a crucial component to establish healthy online communities and ensuring online safety from hate speech and offensive language. In many cases, hate speech may be targeted at specific gender which could be expressed in many different languages on social media platforms such as Indonesian Twitter. However, difficulties such as data scarcity and the imbalanced gender-based hate speech dataset in Indonesian tweets have slowed the development and implementation of automatic social media moderation. Obtaining more data to increase the number of samples may be costly in terms of resources required to gather and annotate the data. This study looks at the usage of data augmentation methods to increase the amount of textual dataset while keeping the quality of the augmented data. Three augmentation strategies are explored in this study: Random insertion, back translation, and a sequential combination of back translation and random insertion. Additionally, the study examines the preservation of the increased data labels. The performance result demonstrates that classification models trained with augmented data generated from random insertion strategy outperform the other approaches. In terms of label preservation, the three augmentation approaches have been shown to offer enough label preservation without compromising the meaning of the augmented data. The findings imply that by increasing the amount of the dataset while preserving the original label, data augmentation could be utilized to solve issues such as data scarcity and dataset imbalance.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135921872","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 : 2023-10-01DOI: 10.3844/jcssp.2023.1253.1262
Amit Kumar Kundu, Rezaul Karim, Ali Ahmed Ave
Support Vector Machine (SVM) is a highly attractive algorithm among many machine learning models due to its generalization power and classification performance based on sound mathematical formulation being convex that offers global minimum. However, despite being sparse, its high classification cost from kernel execution with Support Vectors (SVs) reduces the user's interest when there are hard computational constraints in the application, especially, for large and difficult data. So far in our knowledge, out of many existing works to overcome this problem, some are really interesting and heavy but get less attractive due to improper training difficulties for example, excessive cost-memory requirement, initialization, and parameter selection trouble because of the non-convexity of the problems while the other few that avoid these problems, cannot generate sparsity and complexity simultaneously of the final discriminator upto satisfactory level for very large and tricky data. In this direction, we propose a novel algorithm Efficiency Escalated SVM (EESVM) that solves two convex problems using Quadratic Programming (QP) and Linear Programming (LP) in sequence. This is followed by computational analysis on the remaining smallest set of slack variables that ultimately build two very essential properties of the machine: (i) Highly efficient by being heavily sparse and optimally complex and (ii) Able to handle very large and noise-effected complicated data. Benchmarking shows that this EESVM demands kernel computation as little as 6.8% of the standard QPSVM while posing almost the same classification accuracy on test data and requiring 42.7, 27.7 and 46.6% that of other three implemented state-of-the-art heavy-sparse machines while offering similar classification accuracy. It claims the lowest Machine Accuracy Cost (MAC) value among all of these machines though showing very similar generalization performance that is evaluated numerically using the term Generalization Failure Rate (GFR). Being quite pragmatic for modern technological advancement, it is indispensable for optimum manipulation of the troublesome massive, and difficult data.
{"title":"Escalating SVM-Efficiency by Sequential QP LP and Slack Variable Analysis","authors":"Amit Kumar Kundu, Rezaul Karim, Ali Ahmed Ave","doi":"10.3844/jcssp.2023.1253.1262","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1253.1262","url":null,"abstract":"Support Vector Machine (SVM) is a highly attractive algorithm among many machine learning models due to its generalization power and classification performance based on sound mathematical formulation being convex that offers global minimum. However, despite being sparse, its high classification cost from kernel execution with Support Vectors (SVs) reduces the user's interest when there are hard computational constraints in the application, especially, for large and difficult data. So far in our knowledge, out of many existing works to overcome this problem, some are really interesting and heavy but get less attractive due to improper training difficulties for example, excessive cost-memory requirement, initialization, and parameter selection trouble because of the non-convexity of the problems while the other few that avoid these problems, cannot generate sparsity and complexity simultaneously of the final discriminator upto satisfactory level for very large and tricky data. In this direction, we propose a novel algorithm Efficiency Escalated SVM (EESVM) that solves two convex problems using Quadratic Programming (QP) and Linear Programming (LP) in sequence. This is followed by computational analysis on the remaining smallest set of slack variables that ultimately build two very essential properties of the machine: (i) Highly efficient by being heavily sparse and optimally complex and (ii) Able to handle very large and noise-effected complicated data. Benchmarking shows that this EESVM demands kernel computation as little as 6.8% of the standard QPSVM while posing almost the same classification accuracy on test data and requiring 42.7, 27.7 and 46.6% that of other three implemented state-of-the-art heavy-sparse machines while offering similar classification accuracy. It claims the lowest Machine Accuracy Cost (MAC) value among all of these machines though showing very similar generalization performance that is evaluated numerically using the term Generalization Failure Rate (GFR). Being quite pragmatic for modern technological advancement, it is indispensable for optimum manipulation of the troublesome massive, and difficult data.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922548","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}
Bayesian ranking-based drug-target relationship prediction has achieved good results, but it ignores the relationship between drugs of the same target. A new method is proposed for drug-target relationship prediction based on groups by Appling Bayesian. According to the reality that drugs interacting with a specific target have similarities, a grouping strategy was introduced to make these similar drugs interact. A theoretical model based on the grouping strategy is derived in this study. The method is compared with five typical methods on five publicly available datasets and produces superior results to the compared methods. The impact of grouping interaction on the Bayesian ranking approach is examined in this study to create a grouped medication set; comparable pharmaceuticals that interact with the same target are first grouped based on this reality. Then, based on the grouped drug set, new hypotheses were put forth and the conceptual approach of grouped Bayesian ranking was constructed. Finally, to predict novel medications and targets, the article also includes neighbor information. The associated studies demonstrate that the strategy presented in this study outperforms the conventional performance techniques. Plans for further performance improvement through the creation of new comparable grouping objectives are included in future work.
{"title":"A Drug-Target Interaction Prediction Based on Supervised Probabilistic Classification","authors":"Manmohan Singh, Susheel Kumar Tiwari, G. Swapna, Kirti Verma, Vikas Prasad, Vinod Patidar, Dharmendra Sharma, Hemant Mewada","doi":"10.3844/jcssp.2023.1203.1211","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1203.1211","url":null,"abstract":"Bayesian ranking-based drug-target relationship prediction has achieved good results, but it ignores the relationship between drugs of the same target. A new method is proposed for drug-target relationship prediction based on groups by Appling Bayesian. According to the reality that drugs interacting with a specific target have similarities, a grouping strategy was introduced to make these similar drugs interact. A theoretical model based on the grouping strategy is derived in this study. The method is compared with five typical methods on five publicly available datasets and produces superior results to the compared methods. The impact of grouping interaction on the Bayesian ranking approach is examined in this study to create a grouped medication set; comparable pharmaceuticals that interact with the same target are first grouped based on this reality. Then, based on the grouped drug set, new hypotheses were put forth and the conceptual approach of grouped Bayesian ranking was constructed. Finally, to predict novel medications and targets, the article also includes neighbor information. The associated studies demonstrate that the strategy presented in this study outperforms the conventional performance techniques. Plans for further performance improvement through the creation of new comparable grouping objectives are included in future work.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922321","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 : 2023-10-01DOI: 10.3844/jcssp.2023.1180.1189
Kanmani S, M. Murali
In dynamic communication networks, improvement of energy efficiency is one of the major challenges for reliable communication. By considering this challenge, we focus to develop the topology of the network. In this study, we design a hybrid star-mesh topology for minimizing the latency as well as energy consumption of the network. In the hybrid network topology, the Ad-Hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol is used to establish multiple paths between source and destination. Among the multiple paths, the optimal path is chosen using Chimp Optimization Algorithm (ChOA) when the routing path loses its energy level. The optimal path selection leads to enhancing the energy efficiency of the network. Simulation results discuss the superior performance of the proposed scheme in terms of delivery ratio, energy consumption, delay, and throughput by 7% on aggregate.
{"title":"Proposing a Hybrid Topological Organization for Non-Misbehaving Nodes with Optimal Path Selection Using Game-Theoretic Approach","authors":"Kanmani S, M. Murali","doi":"10.3844/jcssp.2023.1180.1189","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1180.1189","url":null,"abstract":"In dynamic communication networks, improvement of energy efficiency is one of the major challenges for reliable communication. By considering this challenge, we focus to develop the topology of the network. In this study, we design a hybrid star-mesh topology for minimizing the latency as well as energy consumption of the network. In the hybrid network topology, the Ad-Hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol is used to establish multiple paths between source and destination. Among the multiple paths, the optimal path is chosen using Chimp Optimization Algorithm (ChOA) when the routing path loses its energy level. The optimal path selection leads to enhancing the energy efficiency of the network. Simulation results discuss the superior performance of the proposed scheme in terms of delivery ratio, energy consumption, delay, and throughput by 7% on aggregate.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922309","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 Internet of Things(IoT) furnishes a visual blueprint for the future internet. It serves upsensors, actuators, and distal devices on the edge of the network, creating agiant interconnected network. The IoT era refers to the future where all theconceivable data streams are integrated into the IoT, granting human-barrierfree access to physical entities on the internet. Along with the rapid progressof IoT, pressing issues have emerged. Energy dissipation, limited processingefficiency, and confined memory have become severe constraints for the IoT era.Phase Change Memory with Dynamic Random-Access Memory (PCM-DRAM) is ahybrid memory system that has been proven to reduce energy dissipation. It isknown to have a great capacity, higher endurance, and low latency. In thisstudy, we first analyze the significant constraints faced in the IoTdevelopment. We then analyze how these constraints can be solved by PCM-DRAMmemory. To this end, we propose a PCM-DRAM hybrid memory system called“Segment-Aware and Dynamic Partitioning PCM-DRAM” (SADP PCM-DRAM). Our proposalis grounded in a meticulous evaluation of the specific requirements posed byIoT applications. Furthermore, we also proposed two essential equations forquantifying energy consumption and the overall performance in terms ofaverage memory hit time.
{"title":"Segment-Aware Dynamic Partitioning PCM-DRAM: A Solution to IoT Devices Development Constraints","authors":"Qijin Zhu, Shuyi Liu, Zahid Akhtar, Kamran Siddique","doi":"10.3844/jcssp.2023.1212.1221","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1212.1221","url":null,"abstract":"The Internet of Things(IoT) furnishes a visual blueprint for the future internet. It serves upsensors, actuators, and distal devices on the edge of the network, creating agiant interconnected network. The IoT era refers to the future where all theconceivable data streams are integrated into the IoT, granting human-barrierfree access to physical entities on the internet. Along with the rapid progressof IoT, pressing issues have emerged. Energy dissipation, limited processingefficiency, and confined memory have become severe constraints for the IoT era.Phase Change Memory with Dynamic Random-Access Memory (PCM-DRAM) is ahybrid memory system that has been proven to reduce energy dissipation. It isknown to have a great capacity, higher endurance, and low latency. In thisstudy, we first analyze the significant constraints faced in the IoTdevelopment. We then analyze how these constraints can be solved by PCM-DRAMmemory. To this end, we propose a PCM-DRAM hybrid memory system called“Segment-Aware and Dynamic Partitioning PCM-DRAM” (SADP PCM-DRAM). Our proposalis grounded in a meticulous evaluation of the specific requirements posed byIoT applications. Furthermore, we also proposed two essential equations forquantifying energy consumption and the overall performance in terms ofaverage memory hit time.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922313","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}
Technology implementation in the Society 5.0 era has significant consequences for every industry, especially in the freight service sector. Era Society 5.0 emerged in anticipation of disruption due to the industrial revolution 4.0, which would degrade Human Resources (HR). The amount of information that must be managed related to HR raises problems, namely untidy data management, difficulty accessing employee data and difficulty finding pay slips, causing HR to be unable to contribute appropriately to the industry. In addition to experiencing problems with data completeness, the freight service industry is also experiencing problems with employees attending to be fully recorded due to manual data collection to the confusion of performance appraisal, which causes errors in giving bonuses. Based on the background of these problems, a new management solution is needed, namely the Human Resource Information System (HRIS). However, before implementing HRIS, the freight service industry needs to develop an HR management planning strategy using the help of the Plan, Do, Control, Act (PDCA) method. The research phase begins with a literature study and data collection. Mapping business processes and problems experienced is the basis for mapping the HR management strategy for making HRIS. Then, the HRIS realization process is measured by defining the cycles that go through based on the previous strategy design. The results of this study are in the form of proposed strategies for how HRIS impacts industry, especially in the Society 5.0 era, using analysis Performance, Information, Economic, Control, Efficiency, Service (PIECES). This study aims to develop the primary human resource management strategy used in the HRIS of the freight forwarding service industry. Thus, this strategy hopes to be used to face the defiance of the Society 5.0 era.
{"title":"Mapping a Strategic Human Resource Information System for Society 5.0","authors":"Glisina Dwinoor Rembulan, Evaristus Didik Madyatmadja, Johanes Fernandes Andry, Lydia Liliana, Agustina Andriani","doi":"10.3844/jcssp.2023.1283.1291","DOIUrl":"https://doi.org/10.3844/jcssp.2023.1283.1291","url":null,"abstract":"Technology implementation in the Society 5.0 era has significant consequences for every industry, especially in the freight service sector. Era Society 5.0 emerged in anticipation of disruption due to the industrial revolution 4.0, which would degrade Human Resources (HR). The amount of information that must be managed related to HR raises problems, namely untidy data management, difficulty accessing employee data and difficulty finding pay slips, causing HR to be unable to contribute appropriately to the industry. In addition to experiencing problems with data completeness, the freight service industry is also experiencing problems with employees attending to be fully recorded due to manual data collection to the confusion of performance appraisal, which causes errors in giving bonuses. Based on the background of these problems, a new management solution is needed, namely the Human Resource Information System (HRIS). However, before implementing HRIS, the freight service industry needs to develop an HR management planning strategy using the help of the Plan, Do, Control, Act (PDCA) method. The research phase begins with a literature study and data collection. Mapping business processes and problems experienced is the basis for mapping the HR management strategy for making HRIS. Then, the HRIS realization process is measured by defining the cycles that go through based on the previous strategy design. The results of this study are in the form of proposed strategies for how HRIS impacts industry, especially in the Society 5.0 era, using analysis Performance, Information, Economic, Control, Efficiency, Service (PIECES). This study aims to develop the primary human resource management strategy used in the HRIS of the freight forwarding service industry. Thus, this strategy hopes to be used to face the defiance of the Society 5.0 era.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135922315","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 : 2023-10-01DOI: 10.3844/jcssp.2023.1242.1252
Mohamed Ouhda, Zarouit Yousra, Brahim Aksasse
The date palm (Phoenixdactylifera) is a large palm with exotic fruits measuring up to 30 metersin height. The date palm produces fruits rich in nutrients provides a multitudeof secondary products, and generates income necessary for the survival of alarge population. Losses attributed to manual harvesting encompass bothquantitative and qualitative aspects, with the latter measured throughattributes such as appearance, taste, texture, and nutritional or economicvalue. These losses, in terms of both quantity and quality, are influenced bypractices across all phases of the harvesting process. On the other hand, therisks of work accidents are high because of the length of the date palms. Toreduce the losses and reduce risks, it is essential to propose a decisionsystem for robotic harvesting to help farmers overcome the constraints duringthe harvest. The assessment of quality and maturity levels in variousagricultural products is heavily reliant on the crucial attribute of color. Inthis study, an intelligent harvesting decision system is proposed to estimatethe level of maturity based on deep learning, K-means clustering, and coloranalysis. The decision system's performance is assessed using the dataset ofdate fruit in the orchard and various metrics. Based on the experimentalresults, the proposed approach has been deemed effective and the systemdemonstrates a high level of accuracy. The system can detect, locate, andanalyze the maturity stage to make a harvest decision.
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