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An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions

Digital communications and Networks 2023年第1期9卷 79-89页

作者： Varun Kohli Sombuddha Chakravarty Vinay Chamola Kuldip Singh Sangwan Sherali Zeadally Department of Electrical and Computer Engineering Engineering National University of SingaporeSingapore Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Mechanical Engineering BITS-PilaniPilani Campus333031India College of Communication and Information University of KentuckyLexingtonKY40506-0224USA

There is an urgent need to control global warming caused by humans to achieve a sustainable ***_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global *** increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy *** energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and *** latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and *** review compiles estimates made by various studies from 2018 to *** compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as *** identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon ***,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.

关键词： Blockchain Carbon footprint Climate change Cryptocurrency Sustainability

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Intelligent Intrusion Detection System for Industrial Internet of Things Environment

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computer Systems Science & engineering 2023年第2期44卷 1567-1582页

作者： R.Gopi R.Sheeba K.Anguraj T.Chelladurai Haya Mesfer Alshahrani Nadhem Nemri Tarek Lamoudan Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Mathematics College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival *** classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and *** resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 *** CCSOA-OWKELM technique focuses on the design of feature selection with classiﬁcation approach to achieve minimum computation complex-ity and maximum detection *** CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with ***,the OWKELM technique is applied for the intrusion detection and classiﬁcation *** addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunﬂower optimization(SFO)*** utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better *** order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

关键词： Intrusion detection system artiﬁcial intelligence machine learning industry 4.0 internet of things

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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An Enhanced Product Recommendation System Using Decision Tree Algorithm 7th

An Enhanced Product Recommendation System Using Decision Tre...

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7th International Conference on communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Akinyoola, Oluwatimilehin Moses Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany

ISBN: (纸本)9789819764648

Product recommendation systems are a critical element of e-commerce platforms, as they enable customers to identify the items that best meet their needs. Product recommendation systems are important tools used by organizations to increase customer engagement, satisfaction, and loyalty. Existing product recommendation systems have several weaknesses, such as an inability to incorporate features outside of customer preferences. Therefore, this paper proposes an enhanced product recommendation system using a decision tree algorithm. The main objectives of this system are to improve the accuracy and efficiency of the existing product recommendation systems. To accomplish this, a framework includes a data preprocessing phase, a feature selection phase, and a model training phase. The data preprocessing phase is used to clean the data and eliminate any noise. The feature selection phase is used to identify the most informative features, which are then used to train the decision tree model. The model is trained using CART, a supervised learning algorithm, and is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Finally, the model is tested on a test dataset to compare with existing solutions. The results show that the proposed system outperforms existing recommender systems in terms of all the evaluation metrics discussed. Furthermore, the improved system also provides useful insights into the product recommendation process for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Low-power test pattern generator using modified LFSR

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Aerospace Systems 2024年第1期7卷 67-74页

作者： Govindaraj, V. Dhanasekar, S. Martinsagayam, K. Pandey, Digvijay Pandey, Binay Kumar Nassa, Vinay Kumar Department of Electronics and Communication Engineering Dr NGP Institute of Technology Coimbatore641048 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore641202 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Technical Education Uttar Pradesh Kanpur India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Udham Singh Nagar Uttarakhand India Computer Science Engineering Department Rajarambapu Institute of Technology Rajaramnagar Sakharale Tel Walwa Dist Sangli Maharashtra Islampur415414 India

Low-power designs are getting increased significance in numerous applications like high-performance computing and wireless communication due to the rise in power dissipation. Power dissipation of VLSI (very large scale integration) circuits in test mode is much higher than in the normal operation mode due to the high frequency of applied test patterns. Product lifetime, yield, reduced performance, and circuit damage will result from this additional power consumption in testing. Therefore, the main objective of today’s test applications is to minimize power dissipation by increasing the correlation of applied successive test vectors. Low-power test pattern generator (TPG) using LFSR (linear feedback shift register) and binary to the excess-4 converter and binary ripple counter is proposed. The test vectors generated by the TPG have a high correlation between successive test vectors, which leads to minimum switching. During the testing of benchmark circuits, the proposed method shows a significant reduction in dynamic power consumption concerning its peer works. © Shanghai Jiao Tong University 2023.

关键词： Electric power utilization

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M2PAIR: A High-Quality Acoustic Impulse Response Computation Model

M2PAIR: A High-Quality Acoustic Impulse Response Computation...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Zhiyu Zhao, Xinpei Wang, Jing Qian, Xinyuan Xie, Xiang School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Acoustic Impulse Response (AIR) provides crucial spatial information about the environment, significantly enhancing audio immersion. However, achieving high perceptual quality while computing AIR in real-time for interactive audio-video media (IAVM) presents a challenging problem. This study proposes the Mesh to Parametric AIR (M2PAIR), a method for computing AIR designed for IAVM. M2PAIR integrates neural networks with psychoacoustics. It takes the 3D scene mesh, the listener positions, and the sound source positions as inputs, utilizes perceptual parameters as intermediaries, and computes the desired high-quality AIR signal based on these parameters. Experimental results demonstrate that M2PAIR improves the perceptual quality of AIR output compared to existing methods while reducing the model complexity. Additionally, it meets the requirements of IAVM, including real-time computation, high sampling rates, and flexible duration for the output AIR. © 2025 IEEE.

关键词： Acoustic Impulse Response Auralization Deep Learning Interactive Media Psychoacoustics

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A comprehensive survey on the biometric systems based on physiological and behavioural characteristics

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Materials Today: Proceedings 2023年 80卷 2642-2646页

作者： Abdulrahman, Shaymaa Adnan Alhayani, Bilal Department of Computer Engineering Techniques College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Electronics and Communication Yildiz Technical University Istanbul Turkey

With the fast increasing of the electronic crimes and their related issues, deploying a reliable user authentication system became a significant task for both of access control and securing user's private data. Human biometric characteristics such as voice, finger, iris scanning, face, signature and other features provide a dependable security level for both of the personal and the public use. Many biometric authentication systems have been approached for long time. Due to the uniqueness of human biometrics witch played a master role in degrading imposters’ attacks. Such authentication models have overcome other traditional security methods like passwords and PIN. This paper aims to briefly address the psychological biometric authentication techniques and a brief summary to the advantages, disadvantages of each method. Main contribution it found that used EEG signals, as biometrics is the best technique compare to with five other techniques. © 2021

关键词： Biometrics

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Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

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computer Systems Science & engineering 2023年第6期45卷 2635-2645页

作者： J.R.Arunkumar S.Velmurugan Balarengadurai Chinnaiah G.Charulatha M.Ramkumar Prabhu A.Prabhu Chakkaravarthy Department of Information Technology Sree Vidyanikethan Engineering College(Autonomous)TirupatiAndhra PradeshIndia Department of Electronics and Communication Engineering TJS Engineering CollegeKavaraipettaiChennaiTamil NaduIndia Department of Computer Science and Engineering Vidyavardhaka College of EngineeringMysuruKarnatakaIndia Department of Electronics and Communication Engineering Peri Institute of TechnologyChennaiTamil NaduIndia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennaiTamil NaduIndia

Nowadays,Wireless Sensor Network(WSN)is a modern technology with a wide range of applications and greatly attractive benefits,for example,self-governing,low expenditure on execution and data communication,long-term function,and unsupervised access to the *** Internet of Things(IoT)is an attractive,exciting *** applying communication technologies in sensors and supervising features,WSNs have initiated communication between the IoT *** IoT offers access to the highest amount of information collected through WSNs,it leads to privacy management ***,this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique(LRECC)to establish a secure IoT structure for preventing,detecting,and mitigating *** approach uses the Elliptical Curve Cryptography(ECC)algorithm to generate and distribute security *** algorithm is a light weight key;thus,it minimizes the routing ***,the Logistic Regression machine learning technique selects the transmitter based on intelligent *** main application of this approach is smart *** approach provides continuing reliable routing paths with small *** addition,route nodes cooperate with IoT,and it handles the resources proficiently and minimizes the 29.95%delay.

关键词： Wireless sensor network internet of things security elliptical curve cryptography machine learning regression analysis

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Video and image quality improvement using an enhanced optimized dehazing technique

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Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems Science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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