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1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023

作者： Indriyono, Bonifacius Vicky Pamungkas, Natalinda Abbas, Fatima Hashim Mintorini, Ery Almohamadi, Mohammed Mahmood, Sarmad Nozad Alani, Sameer Alkhayyat, Ahmed H. Rachmawanto, Eko Hari University of Dian Nuswantoro Information System Kediri Indonesia Al-Mustaqbal University College Medical Laboratories Techniques Department Hillah51001 Iraq University of Dian Nuswantoro Visual Communication Design Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna66002 Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq University of Mashreq Research Center Medical Laboratories Techniques Baghdad Iraq College of Technical Engineering University of Mashreq the Islamic University Najaf Iraq University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments;they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD. © 2023 IEEE.

关键词： Energy efficiency

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Extraction of corn seed image texture using machine learning method

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AIP Conference Proceedings 2024年第1期2987卷

作者： Yampi R. Kaesmetan Fuzy Yustika Manik Wisard Kalengkongan Informatics Techniques Study Program STIKOM Uyelindo Kupang Kupang Indonesia Faculty of Computer Sciences and Information Technology Universitas Sumatera Utara Medan Indonesia Information System Study Program Universitas Sam Ratulangi Manado Manado Indonesia

Indonesia is the largest corn exporter in the world. Corn (Zea mays I.) Problems in determining the selection of corn seed to replant, especially corn in East Nusa Tenggara, are still a critical issue. The things that affect the quality of corn are found: the seeds are damaged, the seeds are dull, the seeds are dirty, the beans are broken due to the drying process, and the shell of the corn. The determination of the quality of corn grains usually is done manually with visual observation. The manual system requires a long time and produces good quality products that are not consistent due to the limitation of visual fatigue and differences in the perception of each observer. This research uses image texture extraction comparison with statistical methods I orde (color moment) and orde statistics II (GLCM) to select the corn seed. Orde statistics I (color moment) shows the emergence of the value of the degree of gray probability pixels in an image, while orde statistics II (GLCM) shows the relationship between two probability pixels forming a concurrence matrix from the image data. This research is expected to help the process of classification in determining the corn seed. The algorithm k of the nearest neighbor (K-NN) who used to research the classification of the object of the image that will be examined. The results of this study successfully performed using k-Nearest neighbor (k-NN) with a distance of euclidean distance and k=1 with the extraction of the color moment got the highest accuracy is 88%, and the extraction GLCM to characterize the homogeneity of 75.5%, correlation of 78.67%, a contrast of 65.75% and energy of 63.82% with an average accuracy of 70.93%.

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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Advanced Engineering and Technologies (ICONNIC), International Conference on

作者： Bonifacius Vicky Indriyono Natalinda Pamungkas Fatima Hashim Abbas Ery Mintorini Mohammed Almohamadi Sarmad Nozad Mahmood Sameer Alani Ahmed H. Alkhayyat Eko Hari Rachmawanto Information System University of Dian Nuswantoro Kediri Indonesia Medical Laboratories Techniques Department Al-Mustaqbal University College Hillah Babil Iraq Visual Communication Design University of Dian Nuswantoro Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Medical Laboratories Techniques University of Mashreq Research center Baghdad Iraq College of technical engineering University of Mashreq The Islamic University Najaf Iraq Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments; they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD.

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Identification of Maize Leaf Diseases Cause by Fungus with Digital Image Processing (Case study: Bismarak Village Kupang District - East Nusa Tenggara)

Identification of Maize Leaf Diseases Cause by Fungus with D...

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Conference on New Media Studies (CoNMedia)

作者： Marlinda Vasty Overbeek Yampi R. Kaesmetan Fenina Adline Twince Tobing Study Program Informatics Faculty of Techniques and Informatics Universitas Multimedia Nusantara Tangerang Banten Study Program Informatics Techniques STIKOM Uyelindo Kupang Kupang East Nusa Tenggara

ISBN: (纸本)9781728115740

Characteristics in common disease caused by a fungus often makes farmers wrong in giving treatment. Prevention is given too often wrong because it is by direct observation. Therefore in this study, we propose a system to detect disease in maize leaf caused by fungi with a view of segmentation or the shape of the maize leaf-based on digital image processing. The sobel operator we used as a shape features extraction. As for the detection technique, we used multiclass Support Vector Machine algorithm with Radial Basis Function kernel. The results of the identification accuracy of the system are 92 225%.

关键词： Diseases Image edge detection Feature extraction Kernel Support vector machines Fungi Shape

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Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

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Molecular psychiatry 2024年第4期29卷 1216页

作者： Bo-Gyeom Kim Gakyung Kim Yoshinari Abe Pino Alonso Stephanie Ameis Alan Anticevic Paul D Arnold Srinivas Balachander Nerisa Banaj Nuria Bargalló Marcelo C Batistuzzo Francesco Benedetti Sara Bertolín Jan Carl Beucke Irene Bollettini Silvia Brem Brian P Brennan Jan K Buitelaar Rosa Calvo Miguel Castelo-Branco Yuqi Cheng Ritu Bhusal Chhatkuli Valentina Ciullo Ana Coelho Beatriz Couto Sara Dallaspezia Benjamin A Ely Sónia Ferreira Martine Fontaine Jean-Paul Fouche Rachael Grazioplene Patricia Gruner Kristen Hagen Bjarne Hansen Gregory L Hanna Yoshiyuki Hirano Marcelo Q Höxter Morgan Hough Hao Hu Chaim Huyser Toshikazu Ikuta Neda Jahanshad Anthony James Fern Jaspers-Fayer Selina Kasprzak Norbert Kathmann Christian Kaufmann Minah Kim Kathrin Koch Gerd Kvale Jun Soo Kwon Luisa Lazaro Junhee Lee Christine Lochner Jin Lu Daniela Rodriguez Manrique Ignacio Martínez-Zalacaín Yoshitada Masuda Koji Matsumoto Maria Paula Maziero Jose M Menchón Luciano Minuzzi Pedro Silva Moreira Pedro Morgado Janardhanan C Narayanaswamy Jin Narumoto Ana E Ortiz Junko Ota Jose C Pariente Chris Perriello Maria Picó-Pérez Christopher Pittenger Sara Poletti Eva Real Y C Janardhan Reddy Daan van Rooij Yuki Sakai João Ricardo Sato Cinto Segalas Roseli G Shavitt Zonglin Shen Eiji Shimizu Venkataram Shivakumar Noam Soreni Carles Soriano-Mas Nuno Sousa Mafalda Machado Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Rajat Thomas Sophia I Thomopoulos Daniela Vecchio Ganesan Venkatasubramanian Chris Vriend Susanne Walitza Zhen Wang Anri Watanabe Lidewij Wolters Jian Xu Kei Yamada Je-Yeon Yun Mojtaba Zarei Qing Zhao Xi Zhu Paul M Thompson Willem B Bruin Guido A van Wingen Federica Piras Fabrizio Piras Dan J Stein Odile A van den Heuvel Helen Blair Simpson Rachel Marsh Jiook Cha Department of Psychology College of Social Sciences Seoul National University Seoul Republic of Korea. Department of Brain and Cognitive Sciences College of Natural Sciences Seoul National University Seoul Republic of Korea. Graduate School of Medical Science Kyoto Prefectural University of Medicine Department of Psychiatry Kyoto City Japan. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. CIBER of Mental Health (CIBERSAM) Carlos III Health Institute Madrid Spain. Department of Clinical Sciences University of Barcelona Barcelona Spain. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto ON Canada. Department of Psychiatry University of Toronto Toronto ON Canada. Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto ON Canada. Department of Psychiatry Yale University School of Medicine New Haven CT 06510 USA. The Mathison Centre for Mental Health Research & Education Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary AB Canada. Departments of Psychiatry and Medical Genetics Cumming School of Medicine University of Calgary Calgary AB Canada. OCD clinic Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Laboratory of Neuropsychiatry Department of Clinical and Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy. Center of Image Diagnostic Hospital Clínic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Departamento e Instituto de Psiquiatria do Hospital das Clinicas IPQ HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University São Paulo SP

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