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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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Automated Area Based Explosive Trace Detection System

Automated Area Based Explosive Trace Detection System

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Maidoki, Nuradeen ACETEL-National Open University of Nigria Department of Artiftial Intelligent Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Natview Technology Abuja Nigeria

ISBN: (纸本)9798350358155

Explosive based attacks on people and sensitive places in the form of terrorism has become a global challenge that is making organizations such as airports, train station, security agencies and the government to do anything possible to secure the lives of people and some essential infrastructures. Several approaches have been adopted to prevent its occurrence that ranges from the use of animals, analytical means and sensor based approach to manual screaming by human personnel. With the development in the field of Artificial Intelligent, efforts are being made to deploy Sensor base system that leverage of machine learning technology to make the system highly accurate and sensitive in detection of explosive trace within an environment. This study presents the architecture of area base explosive trace detection utilizing deep learning model. The experiment shows the concept of area based explosive trace detection from trace properties of Carbon, Nitrogen, Oxygen and Hydrogen (CNOH). The deep leaning model recorded an accuracy of 98% on the explosive trace data. The system uses this information to detect the presence of explosion within an area. © 2024 IEEE.

关键词： Airport security

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Improving the Ambient Intelligence Living Using Deep Learning Classifier

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computers, Materials & Continua 2022年第10期73卷 1037-1053页

作者： Yazeed Yasin Ghadi Mouazma Batool Munkhjargal Gochoo Suliman AAlsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engieering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Tech University of KoreaGyeonggi-do15073Korea

Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live *** a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s *** can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for *** this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)*** first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode *** convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal *** assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,*** reveal that hybrid method gives more accuracy than single classifier in AAL *** suggested system can assists persons with impairments,assisting carers and medical personnel.

关键词： Ambient assisted living convolutional neural network dimensionality reduction frequency-time features wearable technology

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Trust-aware Cloudlet Federation Model for Secure Service Selection

Trust-aware Cloudlet Federation Model for Secure Service Sel...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Shahid, Vaneeza Nayyer, Muhammad Ziad Ahmed, Usama GIFT University Department of Computer Science Gujranwala Pakistan Government College University Faisalabad Department of Software Engineering Faisalabad Pakistan

ISBN: (纸本)9781665476010

The introduction of Edge computing has pushed the horizon to the edge of the network and the user's proximity. The openness of data on the edge raised many issues about the security of data. Although edge computing is an extension of the cloud, besides this, its solutions to handle security threats are different from cloud computing because of its architectural dissimilarities. There is a need for a comprehensive mechanism to ensure the choice of secure service. For this purpose, the combination of trust would be a better approach to deal with the service selection tasks in edge computing. In this paper, a trust-aware cloudlet federation model has been proposed which ultimately helps in the safe service selection of an edge node. Before using a service 'A' of any edge node, the trust value of the edge node for service 'A' should be calculated, called Service Trust Value (STV). Then the edge nodes with a higher STV for service 'A' are filtered. Lastly, the model will select the edge node with a satisfactory value of trust for service 'A'. This proposed model will be beneficial in secure service selection. © 2023 IEEE.

关键词： Edge computing

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task 8

Quantized and Interpretable Learning Scheme for Deep Neural ...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Maleki, Alireza Lavaei, Mahsa Bagheritabar, Mohsen Beigzad, Salar Abadi, Zahra University of Tehran Department of Technology Management Tehran Iran Electrical and Computer Engineering University of Tehran Tehran Iran University of Cincinnati Electrical Engineering Department Ohio United States Software and Engineering University of St. Thomas Minnesota MN United States Tehran University Electrical Engineering Department Tehran Iran

ISBN: (纸本)9798331531539

Deep learning techniques have proven highly effective in image classification, but their deployment in resource-constrained environments remains challenging due to high computational demands. Furthermore, their interpretability is of high importance which demands even more available resources. In this work, we introduce an approach that combines saliency-guided training with quantization techniques to create an interpretable and resource-efficient model without compromising accuracy. We utilize Parameterized Clipping Activation (PACT) to perform quantization-aware training, specifically targeting activations and weights to optimize precision while minimizing resource usage. Concurrently, saliency-guided training is employed to enhance interpretability by iteratively masking features with low gradient values, leading to more focused and meaningful saliency maps. This training procedure helps in mitigating noisy gradients and yields models that provide clearer, more interpretable insights into their decision-making processes. To evaluate the impact of our approach, we conduct experiments using famous Convo-lutional Neural Networks (CNN) architecture on the MNIST and CIFAR-10 benchmark datasets as two popular datasets. We compare the saliency maps generated by standard and quantized models to assess the influence of quantization on both interpretability and classification accuracy. Our results demonstrate that the combined use of saliency-guided training and PACT-based quantization not only maintains classification performance but also produces models that are significantly more efficient and interpretable, making them suitable for deployment in resource-limited settings. © 2024 IEEE.

关键词： Convolutional neural networks

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Shape from Shading under changing Illumination using a CNN 38

Shape from Shading under changing Illumination using a CNN

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Rate, Keetley Green, Richard University of Canterbury Department of Mathematics and Statistics New Zealand University of Canterbury Department of Computer Science and Software Engineering New Zealand

ISBN: (纸本)9798350370515

This paper presents an alternative method to the classical yet foundational problem of recovering the geometry of a 3D object from a single 2D brightness image, i.e., shape-from-shading. Drawing inspiration from traditional approaches and recent work in single-image 3D reconstruction, we employ a convolutional neural network trained on synthetic images of characteristic shapes as well as high-frequency surfaces to address the shape-from-shading problem. Although the resulting network is less accurate than non-deep learning (DL) approaches, it exhibits greater resilience to common pitfalls, such as variations in light source direction and the presence of noise in the input image. © 2023 IEEE.

关键词： Convolutional neural networks

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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Predicting the Air Pollution Level and Creating an Awareness Platform 5

Predicting the Air Pollution Level and Creating an Awareness...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nuha, M.N. Wishvajith, G.K.N.P. Caldera, Amitha Weerasinghe, N.T. Weeratunga, G.S. Lokuliyana, Shashika Sri Lanka Institute of Infromation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Infromation Technology Department of Computer Systems & Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution. © 2023 IEEE.

关键词： Time series analysis

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Towards Prevention of Sportsmen Burnout:Formal Analysis of Sub-Optimal Tournament Scheduling

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computers, Materials & Continua 2022年第1期70卷 1509-1526页

作者： Syed Rameez Naqvi Adnan Ahmad S.M.Riazul Islam Tallha Akram M.Abdullah-Al-Wadud Atif Alamri Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia

Scheduling a sports tournament is a complex optimization problem,which requires a large number of hard constraints to *** the availability of several such constraints in the literature,there remains a gap sincemost of the new sports events pose their own unique set of requirements,and demand novel *** talking of the strictly time bound events,ensuring fairness between the different teams in terms of their rest days,traveling,and the number of successive games they play,becomes a difficult task to resolve,and demands *** this work,we present a similar situation with a recently played sports event,where a suboptimal schedule favored some of the sides more than the *** introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the ***,we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire *** latter is crucial,since successively playing a large number of games may lead to sportsmen burnout,which must be prevented.

关键词： Sports scheduling optimization constraint programming fatigue analysis

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