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Exploring the Effectiveness of OKRs in Enhancing Company Objectives: A Comparative Study

IEEE engineering Management Review 2025年第2期53卷 110-121页

作者： Criado, Juan M. Gutierrez, Gema Garzas, Javier Cano, Emilio L. De Lena, M. Teresa Gonzalez Moguerza, Javier M. School of Engineering and Computer Science Statistic and Computing Department Mostoles Madrid28933 Spain

The use of management by objectives (MBOs) methodologies, particularly the objectives and key results (OKRs) framework, has gained widespread attention in recent years as a means of improving organizational performance. This article aims to show that OKRs, a key MBO method, can enhance company performance and employee satisfaction. We conducted a comparative and retroactive analysis of MBO methods used in the 20th and 21st centuries, and surveyed 238 people in nine countries and various companies and positions. We analyzed the results of the survey and compared the variables associated with best practices in the OKR methodology. Our study examined a diverse range of companies, positions, qualifications, and ages, which provides an analysis of whether or not OKRs are being used in the organization and a comprehensive understanding of how OKRs can be implemented across in different contexts. By identifying the best variables associated with optimal performance and employee satisfaction, this article provides practical guidance for organizations seeking to adopt OKR methodology. Furthermore, this article can contribute to the development of best practices in the area of management by objectives, particularly in the context of OKRs, leading to more effective and efficient goal-setting practices in organizations. © 2024 IEEE.

关键词： Object recognition

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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A Tale of Side Friction Elements in Dhaka City: From Modeling Real Cases to Revealing Impacts Through Event-Based Simulation

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IEEE Access 2025年 13卷 37344-37360页

作者： Rumi, Ahmed Mahir Sultan Nobel, Hasan Nahiyan Haque, A. H. M. Osama Fahmid, Abu Humayed Azim Toha, Tarik Reza Alaloula, Nouf Abdulrahman Al-Nabhan, Najla Alnamlah, Abdulaziz Abdulrahman S. Islam, A. B. M. Alim Al Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka1000 Bangladesh United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh University of North Carolina at Chapel Hill Department of Computer Science Chapel HillNC27599 United States King Saud University Department of Computer Science Riyadh11451 Saudi Arabia Imam Mohammad Ibn Saud Islamic University Department of Curricula and Teaching Methods Riyadh11564 Saudi Arabia

A common problem on the streets of Dhaka, the capital of a developing country Bangladesh, is the reduction in the effective road width and blockage of roads due to various side friction elements. Despite significant efforts to study the effects of side friction in different contexts, a comprehensive study that efficiently models side friction elements and then analyzes their impacts using a contextually appropriate simulator for heterogeneous non-lane-based traffic in cities like Dhaka is yet to be conducted in the literature. Therefore, in this study, we attempt to fill this gap. Here, based on real data analysis, we list side friction elements that exhibit substantial impacts on the regular traffic flow on the streets of Dhaka, which includes different categories of roadside objects, pedestrians, and non-motorized vehicles. Subsequently, we model the side friction elements using parameters that effectively capture their characteristics. Leveraging the formulated models, we extend our microscopic road traffic simulator, DhakaSim, which has been proven to appropriately simulate the heterogeneous non-lane-based traffic in cities like Dhaka. To the best of our knowledge, the updated simulator is the only simulator capable of simulating heterogeneous traffic of cities like Dhaka while capturing its real inherent complexities, such as a large number of parked vehicles and irregular pedestrian movement. We rigorously validate the updated simulator using Google Maps data and utilize it to conduct an extensive simulation study analyzing the impacts of side friction elements. Our study reveals that the average speed of Dhaka can be reduced by up to 36.55% and the average waiting time can be increased by up to 161.3% due to side friction. Here, pedestrians exhibit a significantly more negative impact on traffic compared to other side friction elements, making them one of the leading causes of congestion on the streets of Dhaka. Moreover, while non-motorized traffic reduces t

关键词： Traffic congestion

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Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Assessing U-Net Architectures for Effective Brain Tumor Segmentation in MRI

Assessing U-Net Architectures for Effective Brain Tumor Segm...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Verma, Akash Yadav, Arun Kumar Department of Computer Science and Engineering India

ISBN: (纸本)9798331529833

Accurately segmenting brain tumors using MRI scans is essential for diagnosing, treatment planning and monitoring treatment. This research compares several U-Net based architectures, including U-Net DIU-Net, DFP-ResUNet, BU-Net, and SEResU-Net, which are implemented and evaluated using BraTS 2018 dataset. Each model integrates unique enhancements to address challenges in brain tumor segmentation, like vanishing gradients, poor feature extraction, & class imbalance and trained using BraTS 2018 dataset, and their performance is evaluated using Accuracy, Dice score, and IoU score. The comparative analysis reveals that while each model has specific advantages, the SEResU-Net and BU-Net architectures attain the highest performance metrics, especially in segmenting TC and ET regions. This evaluation of U-Net variants segment brain tumor highlights their strengths and limitations, offering key insights for advancing brain imaging research and clinical. © 2025 IEEE.

关键词： Deep learning

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ALCTS—An Assistive Learning and Communicative Tool for Speech and Hearing Impaired Students

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computers, Materials & Continua 2025年第5期83卷 2599-2617页

作者： Shabana Ziyad Puthu Vedu Wafaa A.Ghonaim Naglaa M.Mostafa Pradeep Kumar Singh Computer Science Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj11942Saudi Arabia Faculty of Science Al-Azhar UniversityCairo12111Egypt Department of Mathematics Faculty of ScienceAl-Azhar University(Girl’s Branch)Cairo12111Egypt Department of Computer Science and Engineering Central University of JammuJammu and Kashmir181143India

Hearing and Speech impairment can be congenital or *** and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their ***,the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of *** learning devices enable students to access institutional resources and facilities *** proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and *** tool converts the audio signals into sign language videos for the speech and hearing-impaired to follow and converts the sign language to text format for the teachers to *** educational tool for the speech and hearing-impaired is implemented by customized deep learning models such as Convolution neural networks(CNN),Residual neural Networks(ResNet),and stacked Long short-term memory(LSTM)network *** assistive learning tool is a novel framework that interprets the static and dynamic gesture actions in American Sign Language(ASL).Such communicative tools empower the speech and hearing impaired to communicate effectively in a classroom environment and foster *** deep learning models were developed and experimentally evaluated with the standard performance *** model exhibits an accuracy of 99.7% for all static gesture classification and 99% for specific vocabulary of gesture action *** two-way communicative and educational tool encourages social inclusion and a promising career for disabled students.

关键词： Sign language recognition system ASL dynamic gestures facial key points CNN LSTM ResNet

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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

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computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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CLS-3D: Content-Wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection in Autonomous Vehicles

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IEEE Access 2025年 13卷 69840-69856页

作者： Mushtaq, Husnain Latif, Sohaib Saad Bin Ilyas, Muhammad Mohsin, Syed Muhammad Ali, Mohammed Central South University School of Computer Science and Engineering Changsha410083 China Grand Asian University Department of Computer Science and Software Engineering Sialkot51040 Pakistan The University of Chenab Department of Computer Science Gujranwala50700 Pakistan COMSATS University Islamabad Department of Computer Science Islamabad45550 Pakistan Virtual University of Pakistan Standard College of Modern Sciences Lahore55150 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia Abha61421 Saudi Arabia

Accurate 3D object detection is vital in autonomous driving. Single-modal detectors, using either camera or LiDAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LiDAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3D (Content-wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection). This novel framework fuses LiDAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3D object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3D achieves state-of-the-art performance, with 89.52% 3D mAP and 94.08% BEV mAP, outperforming existing methods. © 2013 IEEE.

关键词： Depth perception

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BAAP-FIoT: Blockchain Assisted Authentication Protocol for Fog-enabled Internet of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15681-15696页

作者： Ponnuru, Raveendra Babu Kumar, Sathish A.P. Azab, Mohamed Alavalapati, Goutham Reddy Virginia Tech Virginia Military Institute and Computer Science Department of Computer and Information Science BlacksburgVA United States Cleveland State University Department of Electrical Engineering and Computer Science ClevelandOH44115 United States Virginia Military Institute Department of Computer Science and Information Sciences LexingtonVA24450 United States University of Illinois Springfield One University Plaza Department of Computer Science SpringfieldIL62703 United States

The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users,IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user's identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications. © 2014 IEEE.

关键词： Authentication Protocol

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A Survey for Blockchain-Enabled Resource Management in Edge Networks

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IEEE Access 2025年 13卷 77252-77277页

作者： Boamah, Sharon A. Louis, Jean D. McNair, Janise Gilbert, Juan E. University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Florida Department of Computer and Information Science and Engineering GainesvilleFL32611 United States

As more people and devices come online with the rapid growth of the internet, the need for secure and efficient network resource management becomes increasingly important. The boom of these connections on Next Generation Wireless Networks through the advent of technologies like the Internet of Things has changed the landscape of traditional edge networks. This necessitates a drastic change in the existing edge network infrastructure. As these devices come online, make requests, and go offline, the allocation and management of edge network resources become paramount. Blockchain, with its growing popularity, presents itself as a potential solution. The inherent features of blockchain, such as decentralization, transparency, security, and immutability, make it possible for network providers to manage network resources efficiently. This survey paper delves into the diverse applications of blockchain in edge network resource management, offering a comprehensive discussion of its potential to revolutionize network infrastructure. Unlike other surveys, we focus this work on three specific contexts of blockchain integration for network resource management: Cloud and Edge Computing, Radio Access Networks in Next Generation Wireless Networks, Reinforcement Learning, and Federated Learning. We provide an extensive analysis of recent research advances in these areas, and the main findings are highlighted. Understanding the current use of blockchain and network resource management opens the door to new research and more efficient and secure networks. The open research issues that arise from the combination of blockchain with the management of edge network resources are identified. Finally, the future directions to fuel innovative research are presented in this survey. © 2013 IEEE.

关键词： Blockchains Surveys Cloud computing Network resource management Internet of Things Edge computing Federated learning Peer-to-peer computing Network slicing Wireless networks

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