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2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Abu-Khadrah, Ahmed Issa, Ghassan F. Aslam, Shaukat Shahzad, Muhammad Ateeq, Karamath Hussain, Munawar Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia Skyline University College School of Information Technology Sharjah United Arab Emirates NCBAandE School of Computer Science Lahore Pakistan Riphah International University Department of Computing and Applied Sciences Lahore Pakistan Skyline University Bsit Faculty Sharjah United Arab Emirates

ISBN: (纸本)9781665409209

People have always enjoyed having pets around them, but in today's hectic world, it might be difficult to properly care for pets, particularly fish, which require more attention. When fish owners are out of town, their fish may be left with a feed shortfall or over-feed. Another consideration is the water quality (temperature, PH level, and so on). As a result, a solution is provided in this article to address the aforementioned issues. Two components comprise the proposed system. The first is for fish eating, while the second is for water feature and level monitoring. The first module also includes electrical, automatic, and communication parts that are connected to the Internet of Things (IoT) to allow fish to be fed on a schedule or remotely through a web interface. Microcontrollers with PH, Ammonia, Temperature and other sensors are included in the second module, which is used to monitor the aquarium's environment. © 2022 IEEE.

关键词： Internet of things

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Deep Learning-Driven Resource Allocation for MEC-Enabled UAV Collision Avoidance System

Deep Learning-Driven Resource Allocation for MEC-Enabled UAV...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Khadidja Zairi Bouziane Brik Younes Guellouma Hadda Cherroun LIM Lab. Amar Telidji University Laghouat Algeria Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE DRIVE Laboratory EA1859 University of Burgundy 49 Rue Mademoiselle Bourgeois Nevers France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The Internet of Unmanned Aerial Vehicles (UAVs) envisions critical services like Collision detection and Avoidance among Vehicles (CAVs). These services are typically implemented at the Multi-access Edge Computing (MEC) to enable ultra-low latency communication, ensuring real-time reactions to prevent collisions. To ensure network coverage and optimal connection of UAVs to the nearest MEC host, the CAV must be deployed across all MEC hosts. However, this may impose additional demands on these hots’ available computational and memory resources. We introduce an Artificial Intelligence empowered framework to optimize virtualized resource allocation to the MEC hosts. The framework harnesses the capabilities of Deep Learning (DL) for twofold pivotal objectives: (i) Forecasting UAV Density: the framework predicts the UAV density that each MEC host must accommodate. This anticipatory insight guides resource allocation, adapting it to the anticipated demand dynamics. (ii) Precision in Virtual Resource Allotment: DL is further instrumental in calibrating the precise quantum of virtual resources requisite for the collision detection application to function optimally. This approach ensures the collision avoidance system’s peak efficiency without unduly taxing MEC host computational capacities. Validation of the proposed framework entails empirical analysis. The experimental outcomes underscore the precision of the prediction model and corroborate the resource allocation framework’s efficacy.

关键词： Wireless communication Computational modeling computer architecture Predictive models Autonomous aerial vehicles Resource management Collision avoidance

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ShieldSMS Sentinel: Guardian of Your Digital Communication Bastion

ShieldSMS Sentinel: Guardian of Your Digital Communication B...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Palash Gourshettiwar Rajesh D Thakare Prapti Duddalwar Rushikesh Shete Swapnil Gundewar Prateek Verma Department of Computer Science and Medical Engineering Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India Department of Electronics Engineering Yashavantrao Chavan College of Engineering Nagpur Maharashtra India Department of Electronics Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science and Business Systems St. Vincent Pallotti College of Engineering and Technology Nagpur Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The SMS spam detection Project provides a comprehensive framework for reliably identifying and classifying spam messages sent over SMS communication channels by fusing ML algorithms with NLP techniques. Using EDA, a dataset of labelled SMS messages identified as spam or ham is thoroughly studied to reveal patterns in the word count, sentence count, and message length distributions. Then, unstructured text is transformed into structured data that may be utilized with ML models through tokenization, punctuation and stop word removal, stemming, and other text preprocessing techniques. Feature engineering, which numerically represents text data using vectorization techniques like BoW and TF-IDF, plays a significant role. Various ML algorithms, including Gaussian Naive Bayes, Multinomial Naive Bayes, SVM, and ensemble approaches, are trained and evaluated. Performance metrics, including accuracy and precision, serve as standards for model selection.

关键词： Support vector machines Measurement Industries Medical services Message services Tokenization Numerical models Bayes methods Reliability Standards

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Deep Reinforcement Learning for Network Security Applications With A Safety Guide

Deep Reinforcement Learning for Network Security Application...

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IEEE International Conference on Communications in China (ICCC)

作者： Zhibo Liu Xiaozhen Lu Yuhan Chen Yilin Xiao Liang Xiao Yanling Bu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Informatics and Communication Engineering Xiamen University Xiamen China

Most of the typical reinforcement learning algorithms help wireless devices choose the security policy such as the moving strategy and communication policy by exploring all the possible state-action pairs including the risky policies that cause a severe collision or network disaster. In this paper, we design a safe reinforcement learning algorithm for safety-critical applications (e.g., intelligent transportation systems) to guide the learning agent to avoid exploring risky policies. This algorithm uses Q-network (i.e., a convolutional neural network or a deep neural network) to choose the policy and designs a safety guide to modify the chosen policy that results in dangerous status. More specifically, the safety guide includes a risk alarm module that evaluates the immediate warning value corresponding to the risk of each state-action pair and a G-network that estimates the long-term risk value. By adding the long-term risk value and the long-term expected reward output by the Q-network, this algorithm uses a safety dock to modify the chosen policy. This algorithm uses the immediate warning value to formulate a safe buffer and a risky buffer for the G-network updating to ensure fully exploration in the initial learning process. As a case study, we apply the designed algorithm in a cargo transportation system, in which the experimental results verify the effectiveness of our algorithm compared with the benchmark safe deep Q-network.

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Building Trust Management using Blockchain Technology

Building Trust Management using Blockchain Technology

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Sustainable Computing and Data Communication systems (ICSCDS), International Conference on

作者： R. Jayanthi S. Sundararajan Tanweer Alam Neha Garg Rupesh Roshan Singh M. Udhayamoorthi Department of Commerce Vidhya Sagar Women’s College University of Madras Chennai Tamilnadu SNS College of Technology Coimbatore-35 Tamilnadu Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Block-14 Mittal School of Business Lovely Professional University Jalandhar-Delhi G.T. Road Phagwara Punjab INDIA

For all parties involved in a logistics system, precise data and smart business operations are essential. However, a hurdle to implementation could be posed by a lack of shared trust. Supply chains experience difficulties as a result of a lack of confidence in data sharing, according to several studies. The degree to which each stakeholder has faith in the data they acquire can have a significant impact on management choices. The processing of bitcoin transactions has made extensive use of blockchain technology. It has recently been shown to be successful in building confidence within the Network of *** building trust between entities who would otherwise have been distrustful of each other’s data, blockchain technology permits more seamless and secure data sharing. However, businesses may encounter severe delays and the requirement for significant processing resources if the network is not IoT-optimized. Additionally, Traditional consensus algorithms still have severe limitations on the nodes’ ability to agree. Trust is one of the major concerns in blockchain technology and stands as a severe challenge presently. Confidentiality, privacy, data integrity, and scalability analysis can be identified as the main challenges presently.

关键词： Protocols Scalability Buildings Supply chains Standards organizations Blockchains Safety

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A systematic study on the role of SentiWordNet in opinion mining

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Frontiers of computer science 2021年第4期15卷 165-183页

作者： Mujtaba HUSNAIN Malik Muhammad Saad MISSEN Nadeem AKHTAR Mickael COUSTATY Shahzad MUMTAZ V.B.Surya PRASATH Department of IT Faculty of ComputingThe Islamia University of BahawalpurBahawalpur 63100Pakistan Laboratoire Informatique Image et Interaction(L3i)Facultés des Sciences et TechnologiesUniversity of La RochelleLa Rochelle 17000France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical CenterCincinnati 45229USA Department of Pediatrics University of Cincinnati College of MedicineCincinnati 45229USA Department of Biomedical Informatics College of MedicineUniversity of CincinnatiCincinnati 45267USA Department of Electrical Engineering and Computer Science University of CincinnatiCincinnati 45221USA

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment *** lexicons play an important role in performing several different opinion mining *** efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the ***,one has to explore the available sentiment lexicons and then select the most suitable *** available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion *** SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of *** this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive *** survey will be useful for the researchers contributing to the field of opinion *** features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers *** the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

关键词： opinion mining lexical based resources Senti-WordNet opinion strength

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Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

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SN computer science 2021年第3期2卷 1-10页

作者： Akter, Laboni Ferdib-Al-Islam Islam, Md. Milon Al-Rakhami, Mabrook S. Haque, Md. Rezwanul Department of Biomedical Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Behavior risk Cervical cancer Correlation Feature importance Machine learning

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia computer science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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The Deep Analysis of Accuracy of Performance Level of NBC Algorithm for Diagnosing BC

The Deep Analysis of Accuracy of Performance Level of NBC Al...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： P. Valarmathi M Ramchander Sampathirao Suneetha P. Sasirekha Saad Khudhur Mohammed Ahmed Hussain Department of CSE Mookambigai College of Engineering Department of MCA Chaitanya Bharathi Institute of Technology Hyderabad Department of Computer Science & Systems Engineering Andhra University College of Engineering Andhra University Andhra Pradesh Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

These days, breast cancer is the foremost predominant cancer among ladies around the world and one of the driving causes of cancer-related mortality. In this ponder, an existing Observation, The study of disease transmission, and Conclusion Comes about (Diviner) dataset is utilized to assess the survival rate of breast cancer patients. Numerous of the profound and machine learning strategies accessible nowadays are so great that they can illuminate a wide course of categorization issues based on precise consider of large-scale datasets. These advances are broadly utilized in most biomedical investigate disciplines. Careful information planning, counting both investigation and visualization, offers pivotal bits of knowledge to support basic decision-making within the field. In this article, Soothsayer breast cancer information is classified utilizing machine learning based strategies. Furthermore, a two-step highlight choice strategy utilizing Foremost Component Examination and Change Edge is utilized to extricate pertinent highlights from the dataset. The dataset is classified utilizing Administered and Outfit learning approaches, counting Ada Boosting, XG Boosting, Incline Boosting, Naive Bayes varieties, and Choice Tree calculations, once the chosen highlights have been removed from the breast cancer dataset. The diverse machine learning techniques are totally overviewed with the utilize of train-test portion and k fold cross-validation. In specific, the Choice Tree calculation works exceptionally well, showing an outstandingly tall accuracy rate in both the train-test part and cross-validation procedures. Subsequently, for the Diviner Breast Cancer dataset, this inquire about illustrates the predominance of the choice Tree over the other elective approaches in Administered and Outfit learning procedures and gives a valuable knowledge into moving forward clinical choice and expectation.

关键词： Visualization Accuracy Decision making Boosting Breast cancer Bayes methods Planning

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HGIC: A Hand Gesture Based Interactive Control System for Efficient and Scalable Multi-UAV Operations

HGIC: A Hand Gesture Based Interactive Control System for Ef...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Jinzhou Li Runxiang Jin Chao Shi Lei Xu Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Kent State University Kent OH USA College of Engineering Cornell University Ithaca NY USA Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

As technological advancements continue to expand the capabilities of multi unmanned-aerial-vehicle systems (mUAV), human operators face challenges in scalability and efficiency due to the complex cognitive load and operations associated with motion adjustments and team coordination. Such cognitive demands limit the feasible size of mUAV teams and necessitate extensive operator training, impeding broader adoption. This paper developed a Hand Gesture Based Interactive Control (HGIC), a novel interface system that utilize computer vision techniques to intuitively translate hand gestures into modular commands for robot teaming. Through learning control models, these commands enable efficient and scalable mUAV motion control and adjustments. HGIC eliminates the need for specialized hardware and offers two key benefits: 1) Minimal training requirements through natural gestures; and 2) Enhanced scalability and efficiency via adaptable commands. By reducing the cognitive burden on operators, HGIC opens the door for more effective large-scale mUAV applications in complex, dynamic, and uncertain scenarios. HGIC will be open-sourced after the paper being published online for the research community, aiming to drive forward innovations in human-mUAV interactions.

关键词： Training Technological innovation computer vision Scalability Robot kinematics Computational modeling Control systems Hardware Motion control Faces

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