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Best Lecturer Decision Support system Using Method Analytical Hierarchy Process (AHP) and Simple Adaptive Weighting (SAW) 9

Best Lecturer Decision Support System Using Method Analytica...

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9th International Conference on Electrical Engineering, computer Science and Informatics, EECSI 2022

作者： Pebrianti, Dwi Nurhayati Bayuaji, Luhur Syafrullah, Mohammad Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Aerospace Engineering Gombak Malaysia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Data processing as one of information technology area has wide range of applications including the selection of best lecturer in universities. Best Lecturer Assessment is required by the university to identify qualified human resources and in the future this process will give motivations to the lectures to be more productive and increase the quality of doing their jobs. In Buana Perjuangan University, Karawang, the selection of best lecturer is still conducted manually, where giving result to be subjective and questionable. Additionally, decision support system method has not been applied in determining the best lecturer. In this study, we proposed hybrid method which combines Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to determine the best lecturer by using five criteria, namely attendance at lecture meetings, academic rank, number of publications, additional assignments, and education levels. The data sample used were obtained using a random sampling technique on lecturer data, provided by the University Data and Information Center. The study involves two phases. Phase 1 involves in developing a questionnaire that contains a list of criteria that a best lecturer must have. In Phase 2, an AHP decision matrix is developed, and the candidates will be ranked based on the selected criteria obtained from Phase 1 and their weight values. The result will then be ranked by using SAW to obtain even more accurate decision of best lecturer. The results obtained indicate that the value of the Consistency Index (CI) for all respondents is consistent, 0.8 in average. Then, the CI result is used to determine the Consistency Ratio (CR), where the result is also consistent for all of the respondents,-0.7 in average. These result shows that the proposed method has the same output with the expert (respondents) decisions. © 2022 Institute of Advanced Engineering and Science (IAES).

关键词： AHP Best Lecturer Ranking SAW SPK

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

引用

5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of Engineering & technology 2023.

关键词： Smart city

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Joko Siswanto Hendry Untung Rahardja Irwan Sembiring Erick Alfons Lisangan Muhammad Iman Nur Hakim Feri Wibowo Road Transportation System Engineering Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Information Technology Satya Wacana Christian University Indonesia Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Computer Science Satya Wacana Christian University Indonesia Computer Science and Software Engineering The University of Western Australia Perth Australia Automotive Engineering Technology Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Engineering and Science Muhammadiyah University Purwokerto Banyumas Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

A crucial role in the BRT transportation system’s planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers.

关键词： Deep learning Training Correlation Accuracy system performance Predictive models Data models Planning Stakeholders Long short term memory

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SNPE-SRGAN: Lightweight Generative Adversarial Networks for Single-Image Super-Resolution on Mobile Using SNPE Framework 5

SNPE-SRGAN: Lightweight Generative Adversarial Networks for ...

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5th International Conference on Computing and Applied Informatics, ICCAI 2020

作者： Tampubolon, Hendrik Setyoko, Aji Purnamasari, Fanindia Department of Information System Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia

An image resulting from a low-resolution (LR) camera on the mobile phone has lower quality than a high-resolution(HR) camera on a DSLR. Meanwhile, the HR camera is pricing if compared with the LR camera. How to achieve a single-image quality on LR camera likewise on HR camera becomes essential research in the past years. Addressing this issue can be done by upscaling a single LR image. Recently, the super-resolution generative adversarial network (SRGAN) model is one of the state-of-the-art super-resolution(SR)models employed on single-image SR. However, implementing a deep learning model like SRGAN on a mobile device is challenging in computation power and resources. This study aims to develop a smaller and lower resources model while preserving single-image SR quality on mobile devices. To meet these objectives, we convert, quantize, and compress the SRGAN model on Snapdragon Neural Processing Engine (SNPE) as an example. We then validate the SRGAN on the DIV2K dataset on which improves the model performances. Besides, we conduct experiments on GPU, DSP environment. The experimental result confirmed that SNPE-SRGAN capable of achieves not only HR images' quality but also low latency by 0.06 second and smaller model by 1.7 Mb size running on DSP. Also, the SRGAN-DLC-Quantized running on GPU has a smaller size by 1.7 Mb and lower latency by 1.151 seconds compared with Non-quantized SRGAN-TensorFlow by 9.1 Mb and 1.608 seconds latency. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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Microwave assisted pyrolysis of biomass feedstock fundamentals and the effect of process parameters - A Review 4

Microwave assisted pyrolysis of biomass feedstock fundamenta...

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4th International Conference on Emerging Electrical Energy, Electronics and Computing, ICE4CT 2022

作者： Hamzah, Husam Talib Veluru, Sridevi Hasan, Hikmat Mohammed Abed, Hayder Yousif Mahdi, Husam Salah Hamdullah, Noor Abdulqader Tukarambai, M. Poiba, Venkata Rao Srikanth, R. Department of Chemical Engineering College of Engineering Andhra University Visakhapatnam India Department of Electrical Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Electrical Engineering University of Technology Baghdad Iraq Department of Computer Science and System Engineering College of Engineering Andhra University Visakhapatnam India Electrical and Computer Engineering School of Engineering and Architecture Altinbas University Istanbul Turkey Department of Chemical Engineering Anil Neerukonda Institute of Technology and Sciences Sangivalasa India

Globally, biomass usage as a supply of non-depletable resources materials used in the production of energy at their rawest state is an issue. Pyrolysis is a method of thermally treating biomass that as a consequence in the formation of liquid, products, both solid and gaseous. Elevated heating is required to transform the complicated composition of the biomass's underlying structure matrix into usable products. Heating with a microwave oven has the potential to become a competitive option. to more traditional methods of heating. Owing to its ease of operation and rapid heating rate, it has recently been widely employed in pyrolysis. The goals of this research are to found the principles of MW-assisted pyrolysis action and to examine some critical issues characteristics of operation that have an influence on the yield of a product. This procedure appears to be governed by a number of operational factors for instance, microwave power and temperature degree, the addition of microwave absorbers and their concentration, initial relative humidity, primary sweep gas flow rate/time spent in residence. © Published under licence by IOP Publishing Ltd.

关键词： Biomass

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Comparative analysis of network forensic tools and network forensics processes 2

Comparative analysis of network forensic tools and network f...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ghabban, Fahad M Alfadli, Ibrahim M Ameerbakhsh, Omair Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia Faculty of Engineering School of Computing Johor Malaysia Al-Madinah International University Faculty of Computer Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Network Forensics (NFs) is a branch of digital forensics which used to detect and capture potential digital crimes over computer networked environments crime. Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs) have abilities to examine networks, collect all normal and abnormal traffic/data, help in network incident analysis, and assist in creating an appropriate incident detection and reaction and also create a forensic hypothesis that can be used in a court of law. Also, it assists in examining the internal incidents and exploitation of assets, attack goals, executes threat evaluation, also by evaluating network performance. According to existing literature, there exist quite a number of NFTs and NTPs that are used for identification, collection, reconstruction, and analysing the chain of incidents that happen on networks. However, they were vary and differ in their roles and functionalities. The main objective of this paper, therefore, is to assess and see the distinction that exist between Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs). Precisely, this paper focuses on comparing among four famous NFTs: Xplico, OmniPeek, NetDetector, and NetIetercept. The outputs of this paper show that the Xplico tool has abilities to identify, collect, reconstruct, and analyse the chain of incidents that happen on networks than other NF tools. © 2021 IEEE.

关键词： computer crime

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Digital forensics domain and metamodeling development approaches 2

Digital forensics domain and metamodeling development approa...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ameerbakhsh, Omair Ghabban, Fahad M Alfadli, Ibrahim M Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia School of Computing Faculty of Engineering Johor Malaysia Al-Madinah International University Faculty of Computer and Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Metamodeling is used as a general technique for integrating and defining models from different domains. This technique can be used in diverse application domains, especially for purposes of standardization. Also, this process mainly has a focus on the identification of general concepts that exist in various problem domain and their relations and to solve complexity, interoperability, and heterogeneity aspects of different domains. Several diverse metamodeling development approaches have been proposed in the literature to develop metamodels. Each metamodeling development process has some advantages and disadvantages too. Therefore, the objective of this paper is to provide a comprehensive review of existing metamodeling development approaches and conduct a comparative study among them-eventually selecting the best approach for metamodel development in the perspective of digital forensics. © 2021 IEEE.

关键词： Digital forensics

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Soft Set Based Clustering and Its Comparison on Categorical Data 9

Soft Set Based Clustering and Its Comparison on Categorical ...

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9th IEEE Information technology International Seminar, ITIS 2023

作者： Riyadi Yanto, Iwan Tri Waishiang, Cheah Hidayat, Rahmat Wahyudi, Rofiul Suprihatin Apriani, Ani Universitas Ahmad Dahlan Information System Department Yogyakarta Indonesia Universiti Malaysia Sarawak Faculty of Computer Science & IT Samarahan Sarawak Malaysia Politeknik Negeri Padang Information Technology Department Padang Indonesia Universitas Ahmad Dahlan Islamic Banking Department Yogyakarta Indonesia Institute Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350306835

Categorical data clustering is problematic since it is difficult or complex to determine how comparable the data is. Several methods, most recently centroid-based strategies, have been developed to reduce the complexity of the similarity of categorical data. These methods nevertheless result in lengthy processing durations. Another method, soft set-based clustering (SSC), based on the probability function of multivariate multinomial distributions, is suggested in this article. Soft sets are used to represent the data, and each soft set has a probability for each object. The joint cluster distribution function determines the probability for each object after the multivariate multinomial distribution function. The connected cluster would receive the highest likelihood. Benchmark data sets from UCI machine learning are used to compare the performance of the approach to the baseline techniques. The outcomes demonstrate that the suggested strategy performed better in purity, rank index, and calculation time. © 2023 IEEE.

关键词： Distribution functions

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CIPM: Common identification process model for database forensics field 2

CIPM: Common identification process model for database foren...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Alfadli, Ibrahim M Ghabban, Fahad M Ameerbakhsh, Omair Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia Faculty of Engineering School of Computing Johor Malaysia Al-Madinah International University Faculty of Computer and Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Database Forensics (DBF) domain is a branch of digital forensics, concerned with the identification, collection, reconstruction, analysis, and documentation of database crimes. Different researchers have introduced several identification models to handle database crimes. Majority of proposed models are not specific and are redundant, which makes these models a problem because of the multidimensional nature and high diversity of database systems. Accordingly, using the metamodeling approach, the current study is aimed at proposing a unified identification model applicable to the database forensic field. The model integrates and harmonizes all exiting identification processes into a single abstract model, called Common Identification Process Model (CIPM). The model comprises six phases: 1) notifying an incident, 2) responding to the incident, 3) identification of the incident source, 4) verification of the incident, 5) isolation of the database server and 6) provision of an investigation environment. CIMP was found capable of helping the practitioners and newcomers to the forensics domain to control database crimes. © 2021 IEEE.

关键词： Digital forensics

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