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Detecting Pneumonia Lung Infection From X-Ray Images with Deep Learning

Journal of Physics: Conference Series 2021年第1期1950卷

作者： K Raheja A Goel M Mahajan Scholar Department of Computer Science and Engineering SGT University Gurugram India Professor School of Computing Science and Engineering Galgotias university India Professor Faculty of Engineering and Technology SGT University Gurugram India

Pneumonia is one of the most prominent basis of premature death globally, according to some of the major health institutions like WHO more than 1 billion people around the world get infected and more than 4 million people die prematurely due to pneumonia lung infection. Detection of pneumonia infection requires a lot of medical tests, which can be expensive and time consuming. Deep Learning, the technology which gives computer systems the ability to learn and adapt through unstructured data in order to complex solve real world problems has the potential to make the detection of pneumonia easier, cost effective and less time consuming. The motivation behind this paper is to grasp the criticalness and utilizations of deep computational learning and Convolutional Neural Network (CNN) by executing it so as to recognize Pneumonia by examining a patient's chest x-beam pictures.

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Implementation of GPSR protocol with various mobility models in VANET Scenario

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Journal of Physics: Conference Series 2021年第1期1950卷

作者： K Raheja M Mahajan A Goel Scholar Department of Computer Science and Engineering SGT University Gurugram India Professor School of Computing Science and Engineering Galgotias university India Professor Faculty of Engineering and Technology SGT University Gurugram India

In Vehicular Ad hoc Networks (VANET), topology varies very frequently because vehicles move in high speed. VANET is deployed on road. In GPSR Protocol, as the data packets are forwarded depending upon on the beacon information sent by neighboring nodes, which contain the location of neighbors. Hence nodes move in mobile nature as they change their positions. Mobile pattern of node is reflected by Mobility Models, as they characterize the movement of users who are mobile on the road, with respect to their direction, velocity and location over a period of time. Mobility models are practiced in implementation of protocols and the pattern by which the mobility models reflect real world practices of vehicles on the roads. Using simple pattern randomly, graph constrained mobility models are common practices while doing research. These models donot describe mobility of vehicles in realistic manner. For instance while accelerating and decelerating in the presence of nearby vehicles, these situations greatly affect the Network performance. Selecting the mobility model which is realistic one is the main focus of this paper. To address the challenges such as high mobility of vehicle nodes on the road and random topology, VANET needs a suitable mobility model to obtain improved Packet Delivery Ratio, Throughput, End to End Delay etc. This paper first implements the GPSR Protocol and then GPSR is analyzed by applying different mobility models such as Random Way Point, Gauss Markov, Manhattan Grid, Reference Point Group and Random direction. Results are analyzed by taking the following parameters: Routing overhead, Throughput, PDR and End to End Delivery. The implementation is carried out using NS—2.35 and Bonmotion is used to create mobility models.

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Comprehensive Analysis of Lightweight Cryptographic Algorithms for Battery-Limited Internet of Things Devices

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International Journal of Distributed Sensor Networks 2025年第1期2025卷

作者： Nahom Gebeyehu Zinabu Yihenew Wondie Marye Kula Kekeba Tune Samuel Asferaw Demilew Department of Software Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia aastu.edu.et Associate Professor in School of Electrical and Computer Engineering (SECE) Addis Ababa Institute of Technology (AAiT) Addis Ababa University (AAU) Addis Ababa Ethiopia aau.edu.et Department of Information Technology College of Computing Debre Berhan University Debre Berhan Ethiopia dbu.edu.et

The growth of billions of devices functioning in resource-constrained situations in the Internet of Things (IoT) era poses serious security issues. As a result of their limited processing power efficiency, memory, and battery life, traditional cryptographic algorithms like elliptic curve cryptography (ECC), Rivest–Shamir–Adleman (RSA), and Advanced Encryption Standard (AES) are sometimes too resource-intensive for these devices. Lightweight cryptography has become a crucial field as a remedy, created especially to maintain security while maximizing energy efficiency and reducing resource usage. With an emphasis on contrasting well-known lightweight block ciphers and pointing out areas for future research, this paper examines the most recent developments in lightweight cryptographic methods. In order to synthesize insights into present issues and future goals, it explores cutting-edge methods that improve performance, security, and energy efficiency. Among the main trends that were covered were the trade-offs between resource limitations and security strength, hardware–software co-optimization, block and stream cipher optimization, and hybrid encryption techniques. The study’s conclusion emphasizes how urgently more research is needed to create cryptographic solutions that can sufficiently safeguard IoT devices.

关键词： battery-limited devices block cipher algorithm embedded system security energy-efficient cryptography IoT security lightweight cryptography low-power cryptographic algorithms and performance analysis

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Enhanced Task Scheduling in Cloud computing Using the ESRNN Algorithm: A Performance-Driven Approach

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Internet Technology Letters 2025年第4期8卷

作者： Chintureena Thingom Martin Margala S. Siva Shankar Prasun Chakrabarti R. G. Vidhya Post Doctoral Researcher University of Louisiana at Lafayette Lafayette Louisiana USA Department of Computer Science and Engineering Alliance University Bengaluru India Professor & Director School of Computing and Informatics University of Louisiana Lafayette Louisiana USA Department of CSE KG Reddy College of Engineering and Technology (KGRCET) Hyderabad Telangana India d Research and Publications International Affairs Department of CSE Sri Padampat Singhania University Udaipur India Department of ECE HKBK College of Engineering Bangalore India

One key component of cloud computing that significantly affects performance is task scheduling. Quality of Service (QoS) in networking and the burgeoning information processing economy have spurred interest in the dynamic task scheduling challenge worldwide. Task scheduling is a complicated topic that has been categorized as NP-hard. It is also more difficult to strike a balance and reap the rewards of every facet of cloud computing because the majority of activities in complex environments are frequently managed using dynamic online task scheduling. This manuscript presents an approach for task scheduling in cloud computing technology for enhancing efficiency in enterprise environments. The proposed algorithm is an Effective Residual Self-Attention Recurrent Neural Network (ESRNN) Algorithm. The proposed method's primary goal is to improve system reliability, enhance efficiency, and provide better load standard deviation, make span, and throughput. The issue of managing Directed Acyclic Graph (DAG) jobs in a cloud computing context is resolved by the ESRNN algorithm. The performance of the proposed technique is implemented in the MATLAB platform and is compared with various existing approaches. The proposed approach determines better outcomes compared to existing methods such as Data aware, First-Come-First-Served (FCFS) and Round Robin (RR). In the existing method, throughput is 1, 1.2, 1.4 s, and then the proposed method throughput is 1.6 s. Based on the results, it can be concluded that the proposed approach has higher throughput compared to existing techniques.

关键词： cloud computing technology convergence performance directed acyclic graph enterprise environment quality of service task scheduling

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Critical Comparative Study of Robot Path Planning in Grid-Based Environment

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Journal of Physics: Conference Series 2021年第1期1804卷

作者： R D Dhaniya K M Umamaheswari Research Scholar School of Computing Department of Computer science and Engineering SRM Institute of Science and Technology Kattankulathur-603203 Assistant Professor School of Computing Department of Computer science and Engineering SRM Institute of Science and Technology Kattankulathur-603203

The path planning problem under two-dimensional maps is a fundamental issue in mobile robotics and other applications (unmanned vehicles, and so forth). The traditional algorithms (graph searching, artificial potential field and so forth) are used to find the shortest path that rely on grid-by-grid searching. The main disadvantage of the traditional methods is robot size is not considered and has much redundant in sharp curves which is difficult to guide a robot movement. In this paper, we compared the five baseline approaches of heuristic methods to find the optimal path which reduces the number of turns and time costs to attain a target. The experimental analysis shows that the second-order x derivative has an advantage over other baseline approaches that the search time and the number of turns are reduced.

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Sales prediction using machine learning approaches

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AIP Conference Proceedings 2021年第1期2387卷 140038页

作者： K. Saraswathi N. T. Renukadevi S. Nandhinidevi S. Gayathridevi P. Naveen Assistant Professor (Sr.G) Computer Technology-UG Kongu Engineering College India Assistant Professor Department of Computing Coimbatore Institute of Technology India UG Student BSc (Information Systems) Kongu Engineering College India

For the successful business, several factors are considered and prediction is made for the sales of the product. Here, the sales prediction is proposed to forecast the sales of Rossamann stores using machine learning algorithms. Sales forecasting is done by analyzing customer purchasing behaviour and it plays an important role in modern business intelligence. Forecasting future sales demand is key to business and business planning activities. Forecasting helps business organizations to make improvements, to make changes to business plans and to provide a stock storage solution. Forecast is determined by the use of data or information from past works and the consideration of recognized feature in future. Sales forecasting plays a vital role in strategic planning and market strategy for every company to assess past and present sales statistics and predict potential results. Overall, accurate sales forecasting helps the company to run more productively and efficiently, to save money on forecasts or predictions. In the proposed study, the linear regression and logistic regression model are analyzed and Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) are trained and tested for our dataset. The data is processed to select the features and extract those features. Accurate projections make it easier for the shop to boost demand growth and a higher degree of sales generation. It produces better prediction rate.

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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis

Results in Engineering

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Results in engineering 2025年 26卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Krunal Vaghela Gowrishankar J Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Samim Sherzod Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of Pharmacy The Islamic University Najaf Iraq College of Pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Adjunct Professor Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Faculty of Engineering Nangarhar University Nangarhar Afghanistan

Biochar, a pivotal product of biomass pyrolysis, is crucial in sustainable agriculture, carbon sequestration, and renewable energy systems. This study employs a comprehensive data-driven approach to predict biochar yield using machine learning algorithms. The dataset, comprising 14 chemical, physical, and reaction parameters collected from reputable studies, was processed using outlier detection via the Monte Carlo Outlier Detection (MCOD) algorithm and hyperparameter tuning to optimize model performance. Among the evaluated models, the Decision Tree algorithm emerged as the most robust predictor, achieving an R² value of 0.771, a mean square error (MSE) of 16.182, and an average absolute relative error percentage (AARE%) of 11.848 % in testing. The SHAP analysis identified residence time, pyrolysis temperature, and ash content as the most influential predictors of biochar yield. Simplified explanations and interpretability offered by these models support their practical application. These findings underline the potential of machine learning to optimize biochar production, enhance agricultural systems, and create scalable methods for sustainable waste management, directly contributing to renewable energy and environmental remediation industries.

关键词： Biochar yield Machine learning Biomass pyrolysis Decision tree SHAP analysis

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Efficiënt load balancing by optimized flexi max-min algorithm

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Journal of Physics: Conference Series 2021年第1期1716卷

作者： P Praveen Kumar V Kanchana Devi Student Master of Computer Applications Vellore Institute of Technology Chennai Campus Tamilnadu India Associate Professor School of Computing Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu India

Load Balancing is required to distribute tasks across Virtual Machines on one hand and on the other hand in a cloud Environment. Here, a new task scheduling algorithm: 'Optimized Flexi Max-Min Scheduling Algorithm' is proposed. The algorithm maintains a data structure which is modeled after a Binary Search Tree (BST) for estimations, enhanced searching, task allocation, and migration of tasks. Cloud-Sim is used to model and simulate the cloud computing environment in order to obtain simulated data. The result of this experiment shows that the proposed algorithm outperforms the traditional Max-Min task scheduling algorithms.

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Preliminary investigation of flapping paper inside a file

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IOP Conference Series: Materials Science and engineering 2020年第2期912卷

作者： B T Kannan Research Assistant Professor Advanced Computing Laboratory Department of Aerospace Engineering SRM Institute of Science and Technology Kattankulathur Kancheepuram District 603 203 India

The present paper is about the flapping mechanism of a paper locked inside a file. The investigation consists of visualization of flapping phenomenon and acoustic measurements resulting from the flapping. The normal ceiling fan is used as a source for the flow. The axial flow is converted to wall jet flow by using a surface perpendicular to the axis of the fan. The file placed with an offset from the fan which produces instabilities and in turn triggers the flapping mechanism. A super slow-motion camera is used to record the phenomenon and a microphone is used for acoustic measurements. The time-series results reveal the intermittent nature of the flow. The acoustic measurements reveal the noise sources and their frequencies via Fast Fourier Transformation.

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Secure Routing Protocols for Wireless Sensor Networks

Secure Routing Protocols for Wireless Sensor Networks

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International Conference on computing, Communication & Automation (ICCCA)

作者： Reetu Singh Kajol Kathuria Anil Kumar Sagar MTech Computer Science and Engineering School of Computing Science and Engineering Galgotias University Greater Noida India Professor School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781538669488

Recently, with the improvement of Wireless Sensor Networks (WSNs), numerous new routing protocols have been developed for WSNs. Routing protocols in WSNs, in any case, may vary depend on the application and network systems. Besides, WSNs are presented to various types of security threats. In this manner, it is headachy for us to pick secure routing protocol for application in WSNs. In regard that the decision of secure routing protocol for WSNs is personally related with the application prerequisites and routing protocol qualities and the assaults on routing protocol, there is no ”panacea” secure routing protocol. In this paper we give a review of secure routing protocols that can be utilized as a part of remote sensor systems since it is imperative to give a characterization of the accessible conventions. Along these lines, a few conventions were audited in this paper. The directing conventions can be grouped into two principle classifications to be specific topology based and convention task based. The principle objective of the work proposed in this paper is to give analysts an unmistakable thought regarding the accessible security based directing conventions and their properties.

关键词： Wireless sensor networks Routing protocols Security Sensor systems Topology Network topology

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