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A comparative assessment of OMP and MATLAB for parallel computation

International Journal of Hybrid Intelligent Systems 2024年第1期20卷 57-66页

作者： Dash, Yajnaseni Abraham, Ajith School of Computer Science Engineering and Technology (SCSET) Bennett University India

The prime goal of parallel computing is the simultaneous parallel execution of several program instructions. Consequently, to accomplish this, the program should be divided into independent sets so that each processor can execute its program part concurrently with the other processors. This study compares OMP and MATLAB, two important parallel computing simulation tools, through the use of a dense matrix multiplication technique. The results showed that OMP outperformed the MATLAB parallel environment by over 8 times in sequential execution and 6 times in parallel execution. From this proposed method, it was also observed that OMP with an even slower processor performs much better than MATLAB with a higher processor. Thus, the present analysis indicates that OMP is a superior environment for parallel computing and should be preferred over parallel MATLAB. © 2024 - IOS Press. All rights reserved.

关键词： MATLAB matrix multiplication OMP Parallel computing

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APMVU: Exploit Pentesting of User Account Settings with Arduino-Based Pro Micro USB and Microcontroller 1

APMVU: Exploit Pentesting of User Account Settings with Ardu...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Lalithaditya, Peddinti Somanadh, Mendu Venkat Rani, Pritam Rani, Prity Sachan, Rohit Kumar Bennett University Scset Greater Noida India Gl Bajaj Institute of Technology & Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350376517

In today's technological landscape, where reliance on digital tools and the internet is pervasive, safeguarding data is imperative. As digitalization permeates every aspect of daily life, the demand for cybersecurity solutions is soaring. This increased dependency has correspondingly led to a surge in security breaches, including unauthorized access, phishing, and other cyberattacks. The safest system is the one that is not used-unplugged, locked in a safe, or buried underground. To overcome these concerns, this work proposes an Arduino based Pro Micro Versa USB, which utilizes the Arduino ATmega32u4 chip, a versatile 8-bit microcontroller from Microchip Technology, housed within the Arduino Pro Microchip. We used the Arduino IDE with version 2.3.2 for the Windows 64-bit OS to implement the proposed APMVU. It has an ATmega32U4 microcontroller and a low-power 8-bit RISC processor for its robust features and versatile capabilities. It includes 32 KB of self-programming Flash program memory, 2.5 KB of SRAM, and 1 KB of EEPROM. Our experimental analysis reveals that Arduino based Pro Micro Versa USB successfully automates a sequence of interactions with the Windows OS, manipulating settings and navigating through menus. It emulates human user actions, showcasing the capabilities of microcontroller-based automation for GUI interactions. This proposed approach offers efficiency and consistency, making it valuable for streamlining repetitive tasks and saving time in various applications. © 2024 IEEE.

关键词： Windows operating system

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Currency Detection for Visually Impaired People using Edge Devices

Currency Detection for Visually Impaired People using Edge D...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Kadam, Ujjwal Meena, Arvind Abuzar, Chaudhary Ujjwal Singhal, Gaurav Srivastava, Divya Netaji Subash University of Technology Computer Science and Engineering New Delhi India Bennett University SCSET Greater Noida India

ISBN: (纸本)9798350393415

Visual impairment is a global issue that affects a significant number of individuals. In India, there are nearly 4.95 million blind people and 70 million vision-impaired people and many of them have difficulty reading currency notes and struggle with performing their everyday transactions. There is a need to develop a device that can help them read currency notes and perform their everyday transactions smoothly. In this paper, we have a solution for the same. An edge device, embedded in a cane stick, that can detect the currency notes using a CNN model and provide audio output. We have improved upon an existing INR (Indian National Rupees) currency dataset by adding additional images to it. We trained a CNN model that can classify INR currency images and deployed it on a Raspberry Pi. The TensorFlow model that we have deployed boasts better accuracy compared to other existing models and has been trained over the largest existing INR dataset. © 2023 IEEE.

关键词： Image enhancement

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Big Data-Driven Long Term Accurate CO2 Emission Forecasting Using Advanced Recurrent Neural Networks

Big Data-Driven Long Term Accurate CO2 Emission Forecasting ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Verma, Anil Routhu, Lalit Chandra Chigrupaatii, Rishikant Choubey, Anurag Misra, Rajiv Singh, T.N. Indian Institute of Technology Patna Dayalpur Department of Computer Science and Engineering Daulatpur801106 India Bennett University Scset Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350362480

Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation. © 2024 IEEE.

关键词： Low emission

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Deep Neural Networks Based Security Solution for ATM Transactions 6th

Deep Neural Networks Based Security Solution for ATM Transa...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Joshi, Neeraj Martha, Sheshikala Chaudhary, Shivam Consul, Prakhar Singh, Sushil Kumar SCSET Bennett University Uttar Pradesh Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Engineering Marwadi University Gujrat Rajkot India

ISBN: (纸本)9783031530845

This paper provides you with a description of this new approach for the security of ATM transactions for its subscribers. Looking towards the banking sector, ensuring the safety and security in Automatic Teller Machine (ATM) is a critical factor for ATM transactions. This paper gives a review in brief about the existing literature survey on security arrangements, types of attacks of ATM transaction, together with IR sensors, bio metric identification like (Fingerprint sensors, Iris scanner), modified encryption standards, live monitoring and OTP (One Time Password) notifications, and systems based on IOT applications. This research paper also spotted the restrictions and obstructions of the ongoing security measures, probable threats, types of attacks in transactions and recommends some areas for future research. The insights gained from this research paper can surely be helpful for improving the security system in ATMs and in upgrading the safety measure of overall ATM transactions. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Security systems

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A Comprehensive Study of Case-Based Reasoning in Information Retrieval: An Artificial Intelligence Approach

A Comprehensive Study of Case-Based Reasoning in Information...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Solanki, Surabhi Verma, Seema Biswas, Prolay Roy, Pulakesh Singh, Aparna Sharma, Surbhi Computer Science & Engineering Banasthali Vidyapith Rajasthan Tonk India Scset Bennett University Greater Noida India Education National Institute of Technical Teachers' Training and Research Department of Electrical & Electronics Engineering Bhopal India Computer Science & Engineering Dayananda Sagar University Ramanagara India Computer Science& Engg Brainware University Kolkata India

ISBN: (纸本)9798331508685

Information Retrieval (IR) systems which provide accurate and contextually relevant information to healthcare and law etc. where accuracy is very crucial. Traditional information retrieval methods fundamentally based on keyword matching and statistical models usually difficult to achieve the semantic meaning and contextual nuances of queries. Case-Based Reasoning (CBR), as an artificial intelligence technique, provide advance solutions to enhance IR systems based on a set of past cases through adaptive context-supporting retrieval. By taking advantage of past cases data, CBR allows IR systems to interpret complex context-sensitive queries and offer personalized results. The paper provides an overview of traditional IR models, examines the CBR integration into these systems, and evaluates the impact of CBR on retrieval effectiveness and personalization. And finally, we elaborate on the challenges involved with scaling and adapting cases towards real-time retrieval and provide some future directions for research, including integrating machine learning techniques and hybrid models to enhance the relevance, interpretability, and scalability of CBR-based IR systems. © 2025 IEEE.

关键词： Case based reasoning

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EEWS: energy-efficient multi-objective workflow scheduling in IaaS cloud environments with CP-FPA optimization

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Jaiprakash, Sahani Pooja Badal, Tapas Kumar, Naween School of Computer Science Engineering and Technology (SCSET) Bennett University U.P. Greater Noida India Department of CSE ITER Siksha ’O’ Anusandhan (Deemed to be University) Jagamara Odisha Bhubaneswar India

With the rapid growth of cloud data centers, concerns about energy consumption and its impact on natural resources have become increasingly critical. In response, organizations and global communities are actively pursuing strategies to enhance energy efficiency, particularly in areas such as server operations, cooling systems, and heating technologies. This paper introduces a novel approach to workflow scheduling in cloud environments, leveraging the MaxUtil model. At its core is the Critical Path-based Flower Pollination Algorithm (CP-FPA), a robust metaheuristic designed to minimize energy consumption while simultaneously optimizing makespan. The proposed methodology operates in two key phases: (i) allocating tasks to active virtual machines and (ii) scheduling these tasks using optimal or near-optimal heuristics. The model incorporates a dynamic multi-objective fitness function and an efficient pollen representation mechanism. Extensive computational experiments were conducted across five scientific applications, demonstrating the algorithm’s rapid convergence and the effectiveness of the proposed solution. Comparative analysis against established scheduling algorithms such as PSO, GSA, GA, BAT, and ACO reveals that the CP-FPA approach consistently outperforms these methods in various scenarios. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Critical path Energy efficient Flower-pollination Makespan Optimizing scheduling Workflow

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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

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computer Modeling in engineering & sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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Detection of Hate Speech and Offensive Language Using Machine Learning and Deep Learning for Multi-Class Tweets

Detection of Hate Speech and Offensive Language Using Machin...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Bhavika Garg Aditya Bhardwaj Tarun Jain Department of Computer Science and Engineering Manipal University Jaipur School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

Nowadays, every single person is indulged in social media, and because of its hidden characteristics, people are free to express their views and thoughts and expressing their views freely is their right. Expressing their thoughts and views on dynamic news puts a positive impact in economy, as it shows how people relate to each other. Yet, there are times when individuals throw toxic comments or accusing them on the grounds of caste, religion, gender identity, ethnicity etc. is a harassment of the given freedom. Due to this, hate and offensive language has become the serious issue, in modern society, which leads to damaging the people's peace, their human rights as well as creating an inequality in society. In this research paper, the dataset has been taken from the Kaggle source, sentiment analysis will be done on the detection of hate speech and offensive language, and the classification will be done on the following three labels: Hate Speech, Offensive Language and Neither.

关键词： Deep learning Sentiment analysis Renewable energy sources Analytical models Tongue Social networking (online) Hate speech

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