31 research outputs found
ANALISIS ISI PERSONAL BRANDING CHANEL YOUTUBE ATTA HALILINTAR PERIODE 1 DESEMBER 2019 – 1 JANUARI 2020
No full textAtta Halilintar is Indonesian No.1 Youtuber. At a relatively young age,
Muhammad Attamimi Halilintar, has become a successful entrepreneur and
YouTuber who reap huge profits. As of March 2019, YouTube channel Atta
Halilintar already has 21 million subscribers. The problem in this study is because
the acquisition of Atta Halilintar, is very spectacular and phenomenal among the
people. How can he get that subscriber as fast and as much, and become YouTuber
no. 1 in Indonesia. Atta's acquisition is relatively fast because he obtained it at a very
young age, 25 years. Being Youtuber no.1 in Indonesia automatically makes him a
millionaire who must be swaying with wealth. This study aims to find out what
personal branding is shown by Atta Halilintar on its YouTube channel. The reason
the author chose the period 1 December 2019 - 1 January 2020 in the study is
because at that period Atta won 21 Million Subscriber and entered into Youtube
rewind 2019. Youtube rewind is an award for the most prominent Youtubers in the
world. In Youtube Rewind mentioned the name Atta Halilintar as "The first
Indonesian creator to hit 20 million subscribers".
The method used in this study is a descriptive content analysis method with
a quantitative approach, which analyzes 28 videos contained on the Atta Halilintar
youtube channel in the period 1 December 2019 - 1 January 2020. The theory used is
8 concepts of the formation of personal branding consisting of specialization,
Leadership, Personality, Difference, Visibility, Unity, Firmness & Good Name.
The results of the research that have been done show that the Atil Halilintar
concept in forming Personal Branding is more dominant using the Personality
Concept, with a percentage of 71.4%. In accordance with Personality Branding
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Theory of Personality, Atta Halilintar dominantly shows the personality as it is.
Energetic, fun & entertaining personality.
Key Word: Atta Halilintar, Youtuber, Personal Branding, Content analysi
Effect of turbulence modeling on hydrodynamics of a turbulent contact absorber
No full textA computational fluid dynamics (CFD) study is conducted to find a suitable two equation turbulence model for accurate prediction of hydrodynamics of an inhouse turbulence contact absorber (TCA) at high gas and liquid velocities. Based on the multi-fluid Eulerian approach, hydrodynamics of TCA is simulated by incorporating three turbulence models i.e. standard k-epsilon model, RNG k-epsilon model and SST k-omega model in ANSYS Fluent (R). The solid phase stresses were closed by using the kinetic theory of granular flows (KTGF). TCA hydrodynamics parameters; expanded bed height and bed pressure drop were used to compare the results of this study with experimental data and also with earlier numerical study published with laminar viscous model. It was found that the RNG k-epsilon model predicted the bed height and pressure drop better than its counterparts. To accurately find the effects of secondary phase turbulence, two RNG k-epsilon model options i-e. per phase and dispersed were also evaluated. The results show that the per phase option of RNG k-epsilon model produced the expanded bed height and pressure drop in close agreement with available experimental data at similar operating conditions.Pakistan Institute of Engineering and Applied Sciences (PIEAS)Second author acknowledges the fellowship she received from Pakistan Institute of Engineering and Applied Sciences (PIEAS) to complete her MS in Process Engineering. The authors are grateful to Dr. Muhammad Zaman and Dr. Muhammad Nadeem for helpful technical discussions
Living in the face of menacing ‘unreason’ - Martin Amis's "The Second Plane" as a response to ideological fundamentalisms
No full textThis work touches upon Martin Amis’s diagnosis of the Western world and its cultural foundations
which seem to have been threatened, as maintained by the author, by a specific form of “de-
Enlightenment” (2008). Amis, a repository of Western intellectual ethos, steps to the fore to
defend reason. In view of the world’s unrests, he fosters a thorough investigation of public beliefs,
either of a religious or political nature, highlighting how deeply individual freedom has been
censored and imperiled by various fundamentalisms. In his highly controversial, often blasphemous,
collection of essays and short stories titled The second plane Amis renounces in an uncompromising
way religious militancy, intellectual insularity, and political dogmatism. Politically
incorrect and willfully offensive, the novelist appears unsparing in his criticism of Islamic integrity
and right-wing ‘theological’ intransigence. My intention, thus, is to discuss Amis’s overall
standpoint referring both to the short story “The last days of Muhammad Atta” and a number of
his articles written between 2002-2007
Enhancing Smart Outdoor Object Navigation for the Visually Impaired via YOLOv10 With Neighbor Coordinates and C2FCIB Attention Mechanism
No full textThis research introduces an AI-driven outdoor object detection system aimed at enhancing navigation for visually impaired individuals (VIIs). VIIs often face significant challenges in accessing and interpreting visual information. Recent advancements in computer hardware and deep learning techniques have led to notable progress in developing assistive technologies for VIIs. However, existing datasets often focus on single scenarios and lack sufficient annotations to represent the diverse obstacles encountered in real-world settings. This limitation hinders the development of comprehensive object detection systems tailored to the needs of VIIs. The system utilizes advanced models such as YOLOv8 (Nano, Small, Medium), YOLOv9c, and YOLOv10n, with neighbor coordinates and C2FCIB attention modules trained on the WOTR dataset, which includes 20 classes of common outdoor objects. A comparative study evaluated the performance of these models across key metrics. The YOLOv8m model demonstrated balanced performance with an accuracy of 85.53. YOLOv8n showed slightly lower performance, with an accuracy of 77.05%, and the YOLOv8s model recorded an accuracy of 84.99%, precision and recall of 0.99, 0.89, matching YOLOv8n with an F1 score of 0.74. Similarly, YOLOv9c achieved an accuracy of 79.82%, and the proposed YOLOv10n model with neighbor coordinates and C2FCIB attention modules led with the highest accuracy of 89.33%, precision and recall of 0.99, 0.92, with F1 score of 0.79. A comparative analysis revealed that the proposed YOLOv10n with neighbor coordinates and C2FCIB attention modules achieved the highest accuracy with precision and recall indicating its reliability for assistive applications. In the realm of assistive technologies, similar AI-powered devices have been developed to aid visually impaired individuals. These innovations, alongside the described object detection system, exemplify the potential of AI in creating inclusive solutions that empower visually impaired individuals to navigate their environments more safely and independently
Factors influencing Outcome of Extradural Hematoma in a Tertiary Care Hospital of Dera Ghazi Khan, Pakistan
No full textBackground: The outcome for this neurosurgical problem is still far from set target in many developing countries like Pakistan. Major proportion of cases presenting with EDH in hospital still has poor outcome. This poor outcome of EDH is attributed to many factors including weak health systems of most developing countries. Objective of study was to analyze factors influencing outcome among patients of head injury with an extradural hematoma before surgery admitted in neurosurgery department of Dera Ghazi Khan Medical College, Dera Ghazi Khan.
Materials and Methods: This cross-sectional analytical study was conducted in neurosurgery department of Dera Ghazi Khan Medical College from January 2019 to December 2019 after ethical approval. All the patients with extradural hematoma of either gender admitted in the department during the study duration in which surgery was performed to evacuate extradural hematoma were included in the study. Data was collected by using preformed, pretested questionnaire. A vital signs and Glasgow coma scale record was maintained at thirty minutes interval. Computerized tomography was done in every patient. The EDH volume was calculated by using Peterson and Epperson equation a x b x c x 0.5. Data was entered and analyzed by using SPSS version 22. Chi square test was applied to observe any statistically significant difference between various strata if existed and p value <0.05 was taken as significant.
Results: Total 237 patients with Extradural Hematoma (EDH) were admitted in neurosurgery department during the study period were included in study. More than half 136 (57.4%) patients were more or equal to the age of 18 years. Majority of the patients 218 (91.9%) in the study were male. Major cause of extradural hematoma among patients in this study was road traffic accident 154 (64.9%). The outcome of EDH was found to be significantly (p <0.001) associated with age of patients. More than ninety percent of the patients who were directly admitted to tertiary care hospital has good outcome as compared to 109 (60.2%) patients which were referred and difference in outcome was statistically significant (p<0.001). The volume of EDH is not significantly associated with the outcome (p=0.090). The GSC score of the patients at the time of admission is significantly associated with the outcome (p<0.001). Study findings showed that GCS score of the patients at the time of surgery was also found to be significantly associated with outcome of EDH (p<0.001).
Conclusion: There is a strong association of outcome in extradural hematoma with age, gender and GCS of the patient. In higher GCS the outcome was excellent but in low GCS the outcome was poor
Sine Cosine Algorithm for Enhancing Convergence Rates of Artificial Neural Network: A Comparative Study
No full textArtificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30 %, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.
Manuscript Received: 26 December 2023, Accepted: 24 January 2024, Published: 15 September 2024, ORCiD: 0000-0003-1718-703
Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification
No full textThe Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches
Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification
No full textThe Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches
Detecting Black Hole Attack using Support Vector Machine with XGBoosting in Mobile Ad-Hoc Networks
No full textMobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this study is to propose a solution for detecting black hole attacks using Extreme Gradient Boosting (XGBoost) based on a Support Vector Machine (SVM), the system for detection seeks to examine network traffic and spot anomalies by examining node activities. Attacking nodes in black hole situations exhibit specific behavioural traits that set them apart from other nodes, the traffic under a black hole attack is created using an NS-2 simulator to test the effectiveness of this strategy, and the malicious node is then identified based on the classification of the traffic into malicious and non-malicious. The results of the proposed technique outperformed the existing machine learning techniques such as Neural Network (NN), SVM, k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), AdaBoost-SVM in terms of accuracy score as it achieved 98.67% as well as other classification performance measures (Precision, Recall, and F-measure)
Detecting Black Hole Attack using Support Vector Machine with XGBoosting in Mobile Ad-Hoc Networks
No full textMobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this study is to propose a solution for detecting black hole attacks using Extreme Gradient Boosting (XGBoost) based on a Support Vector Machine (SVM), the system for detection seeks to examine network traffic and spot anomalies by examining node activities. Attacking nodes in black hole situations exhibit specific behavioural traits that set them apart from other nodes, the traffic under a black hole attack is created using an NS-2 simulator to test the effectiveness of this strategy, and the malicious node is then identified based on the classification of the traffic into malicious and non-malicious. The results of the proposed technique outperformed the existing machine learning techniques such as Neural Network (NN), SVM, k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), AdaBoost-SVM in terms of accuracy score as it achieved 98.67% as well as other classification performance measures (Precision, Recall, and F-measure)
