This document summarizes a research paper that proposes using iterative filtering (IF) algorithms to securely aggregate sensor data in wireless sensor networks in the presence of collusion attacks. Collusion attacks occur when nodes secretly or illegally agree to corrupt transmitted data, causing a mismatch in data aggregation. The paper aims to implement IF algorithms to avoid collusion attacks. IF algorithms work by repeatedly applying a function, using the output of one iteration as the input for the next. This helps maximize the likelihood of inferring accurate data from partially observed systems. The document outlines the methodology, benefits of IF algorithms, and basic steps of how they are implemented.
Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Funct...
This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the
functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.
A Defect Prediction Model for Software Product based on ANFIS
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
The Network simulator helps the developer to create and simulate new models on an arbitrary network by specifying both the behavior of the network nodes and the communication channels. It provides a virtual environment for an assortment of desirable features such as modeling a network based on a specific criteria and analyzing its performance under different scenarios. This saves cost and time required for testing the functionality and the execution of network. This paper has surveyed various Wireless Network Simulators and compared them.
A Survey on Data Intrusion schemes used in MANETIRJET Journal
The document discusses data intrusion schemes used in mobile ad hoc networks (MANETs). It reviews common problems with data intrusion in MANETs due to their dynamic architecture and limited resources. Several proposed intrusion detection schemes are described, including distributed and cooperative schemes, specification-based schemes, and the proposed Random Walker Detection method. The proposed method aims to efficiently detect intrusions by deploying detection engines at each node and excluding detection engines from random walkers to reduce detection latency. It is described as working on three network layers and using advanced encryption standards to securely detect and route around malicious nodes.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
This document summarizes a research paper that analyzes machine learning algorithms for intrusion detection using the UNSW-NB15 dataset. It compares the performance of classifiers like KNN, SGD, Random Forest, Logistic Regression, and Naive Bayes, both with and without feature selection. Chi-Square feature selection is applied to reduce irrelevant features before training the classifiers. The classifiers' performance is evaluated based on metrics like accuracy, precision, recall, F1-score, true positive rate and false positive rate. The paper finds that feature selection can improve classifiers' performance for intrusion detection.
IRJET- Intrusion Detection using IP Binding in Real NetworkIRJET Journal
This document summarizes a research paper that proposes using genetic algorithms and support vector machines to improve network intrusion detection. It discusses how genetic algorithms can be used to select optimal features for support vector machine classifiers, in order to speed up training time and improve classification accuracy. The genetic algorithm optimizes the crossover and mutation probabilities during evolution to find the best feature subset for identifying network intrusions using support vector machines. Evaluation of this approach suggests it could enhance the effectiveness of intrusion detection systems.
Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Funct...Eswar Publications
This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the
functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Survey on Wireless Network SimulatorsjournalBEEI
The Network simulator helps the developer to create and simulate new models on an arbitrary network by specifying both the behavior of the network nodes and the communication channels. It provides a virtual environment for an assortment of desirable features such as modeling a network based on a specific criteria and analyzing its performance under different scenarios. This saves cost and time required for testing the functionality and the execution of network. This paper has surveyed various Wireless Network Simulators and compared them.
The document discusses data fusion techniques for underwater wireless sensor networks. It begins with definitions of data fusion and describes some of the challenges of underwater sensor networks including limited bandwidth and power. It then reviews common data fusion algorithms like Bayesian and neural networks methods and discusses applications like target tracking. Finally, it summarizes several papers that apply data fusion for underwater navigation, target recognition, and energy efficiency.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
This document discusses multisensor data fusion for defense applications. It describes data fusion as integrating data from multiple sensors to provide a more complete picture than from individual sensors alone. Some key defense applications discussed include surveillance, intelligence analysis, and missile guidance systems. The document also provides an example of using a Kalman filter for multisensor data fusion to estimate the state of a moving target tracked by multiple sensors.
Abstract
The exponential growth of knowledge in the World Wide Web, has understood the need to develop economical and effective ways for organizing relevant contents. In the field of web computing, document clustering plays a vital role and plays an interesting and challenging problem. Document clustering is mainly used for grouping the similar documents in the search engine. The web also has rich and dynamic collection of hyperlink information. The retrieval of relevant document from the internet is the complicated task. Based on the user’s query the document will be retrieved from the various databases to give relevant information and additional information for the given query. The documents are already clustered based on keyword extraction and stored in the database. The probabilistic relational approach for web document clustering is to find the relation between two linked pages and to define a relational clustering algorithm based on probabilistic graph representation. In document clustering, both content information and hyperlink structure of web page are considered and document is viewed as a semantic units. It also provides additional information to the user.
Keywords: Document Clustering, Agglomerative Clustering, Entropy, F-Measure
The document provides a survey of optimization algorithms that have been used in cryptography. It discusses how optimization algorithms can help generate cryptographic keys. Several studies are summarized that have used genetic algorithms, differential evolution, ant colony optimization, particle swarm optimization, and other algorithms to optimize keys for encryption algorithms like AES, ECC, and RSA. The purpose is to develop optimal cryptographic keys that improve security and efficiency for real-world applications.
Multi sensor data fusion system for enhanced analysis of deterioration in con...Sayed Abulhasan Quadri
This document proposes a multi-sensor data fusion system to enhance the analysis of concrete deterioration due to alkali-aggregate reaction (AAR). The system uses different sensor types like acoustic, electro-mechanical, optical, and embedded sensors to collect internal and external damage data. Feature extraction and a decentralized Kalman filter are used to fuse the heterogeneous sensor data. An artificial neural network then characterizes and quantifies the damage levels. The study expects to improve accuracy over single sensor systems and establish correlations between surface damage, internal damage, and gel concentration levels causing structural deterioration.
Performance analysis of binary and multiclass models using azure machine lear...IJECEIAES
Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.
A NOVEL EVALUATION APPROACH TO FINDING LIGHTWEIGHT MACHINE LEARNING ALGORITHM...IJNSA Journal
Building practical and efficient intrusion detection systems in computer network is important in industrial areas today and machine learning technique provides a set of effective algorithms to detect network
intrusion. To find out appropriate algorithms for building such kinds of systems, it is necessary to evaluate various types of machine learning algorithms based on specific criteria. In this paper, we propose a novel evaluation formula which incorporates 6 indexes into our comprehensive measurement, including precision, recall, root mean square error, training time, sample complexity and practicability, in order to
find algorithms which have high detection rate, low training time, need less training samples and are easy
to use like constructing, understanding and analyzing models. Detailed evaluation process is designed to
get all necessary assessment indicators and 6 kinds of machine learning algorithms are evaluated.
Experimental results illustrate that Logistic Regression shows the best overall performance.
Dependable fire detection_system_with_multifunctioBharath Kumar
This document proposes a new fire detection system that uses a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism. The AI framework includes multiple machine learning algorithms and an adaptive fuzzy algorithm to improve fire detection accuracy by analyzing data from multiple fire sensors. A Direct-MQTT approach based on SDN is also introduced to reduce data transfer delays by minimizing queuing delays that occur with traditional centralized MQTT brokers. The proposed system achieved over 95% accuracy in fire detection and reduced average end-to-end delays by 72% compared to existing systems.
IRJET - Survey on Clustering based Categorical Data ProtectionIRJET Journal
This document summarizes and compares various techniques for clustering and protecting categorical data to preserve privacy. It discusses subtractive clustering, robust hierarchical clustering, decision tree clustering, outlier detection methods like statistical, depth-based, distance-based and density-based techniques. It also covers protection algorithms such as L-diversity and an evolutionary optimization approach. Tables are provided to compare the benefits and drawbacks of different clustering, outlier detection and protection algorithms for categorical data privacy. The document concludes that clustering is useful for categorical data privacy but challenges remain in improving utility and security.
This document summarizes a research article that proposes a new image steganography technique based on ant colony optimization (ACO) algorithm. The technique aims to increase data hiding capacity and optimize image quality. It uses integer wavelet transform to transform image coefficients, then applies an ACO algorithm to find optimal coefficients for embedding secret data. Experimental results on sample images showed no visual difference between original and stego images, demonstrating the technique increases robustness and embedding capacity without degrading image quality. The proposed technique is compared to existing methods based on peak signal-to-noise ratio, showing it provides higher PSNR values and better image quality.
IRJET - Detection of False Data Injection Attacks using K-Means Clusterin...IRJET Journal
This document discusses detecting false data injection attacks in networks using k-means clustering. It proposes a system that uses a camera to detect inside attacks on a sub-network. When an outside person pauses the camera for a certain period of time, the server will detect this as an inside attack and inform the administrator. The system aims to improve network security by identifying these inside attacks using k-means clustering algorithm to classify sensor measurements and detect false data injected by attackers.
This document discusses detecting false data injection attacks using k-means clustering. It begins with an abstract that describes implementing detection of inside attacks in a sub-network using cameras. When an outside person pauses the camera for a specific amount of time, the server can detect this as an inside attack and notify the administrator. The document then reviews related work on cyber attacks against power grids and state estimation. It proposes a system using cameras to monitor for inside attackers pausing cameras. When this occurs, the server will detect an inside attack and inform the administrator. The key algorithm discussed is k-means clustering to classify sensor data and detect attacks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Online stream mining approach for clustering network trafficeSAT Journals
Abstract A large number of research have been proposed on intrusion detection system, which leads to the implementation of agent based intelligent IDS (IIDS), Non – intelligent IDS (NIDS), signature based IDS etc. While building such IDS models, learning algorithms from flow of network traffic plays crucial role in accuracy of IDS systems. The proposed work focuses on implementing the novel method to cluster network traffic which eliminates the limitations in existing online clustering algorithms and prove the robustness and accuracy over large stream of network traffic arriving at extremely high rate. We compare the existing algorithm with novel methods to analyse the accuracy and complexity. Keywords— NIDS, Data Stream Mining, Online Clustering, RAH algorithm, Online Efficient Incremental Clustering algorithm
IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized...IRJET Journal
This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
IRJET-An Efficient Model for Detecting and Identifying Cyber Attacks in Wirel...IRJET Journal
This document proposes a deep learning model for detecting and identifying cyber attacks in wireless networks. The model uses an autoencoder and random forest classifier to extract features from network data and classify behaviors as malicious or benign. The model was evaluated on the NSL KDD Cup dataset and achieved an accuracy of 82% in detecting attacks. Key aspects of the model include using a non-symmetric deep autoencoder for feature extraction and dimensionality reduction, identifying and removing low frequency attacks from the data, and evaluating performance metrics like detection rate and precision.
IRJET- An Efficient Model for Detecting and Identifying Cyber Attacks in Wire...IRJET Journal
This document proposes an efficient model for detecting and identifying cyber attacks in wireless networks using deep learning approaches. The model is designed to perform feature selection and classification on network data to detect malicious behavior. The model architecture includes input, hidden, and output layers for feature extraction, and uses a random forest classifier trained on the NSL KDD Cup dataset. Experimental results using the KDD Cup and NSL-KDD datasets show the model can accurately classify network behaviors and detect cyber attacks with over 82% accuracy.
IRJET- Machine Learning based Network SecurityIRJET Journal
The document discusses using machine learning algorithms to classify network traffic as malicious or non-malicious. It describes capturing packets from a dummy website under distributed denial of service (DDoS) attack to create a dataset. Two machine learning algorithms, naive Bayes and support vector machines (SVM), are used to classify the network traffic. Both algorithms achieved over 98% accuracy in detecting spam traffic. The paper proposes creating a real-time network traffic classification system using machine learning algorithms to improve network security.
Energy Efficinet Intrusion Detection System in mobile ad-hoc networksIJARIIE JOURNAL
This document summarizes a proposed energy efficient intrusion detection system for mobile ad-hoc networks. It begins with an introduction to intrusion detection systems and mobile ad-hoc networks. It then discusses related work on intrusion detection in mobile ad-hoc networks. The proposed system uses an "impact factor" calculation to select cluster heads in an energy-efficient manner while preventing selfish behavior. Cluster heads run the intrusion detection system using a watchdog method to detect misbehaving nodes. Simulation results show that forming clusters reduces energy consumption compared to all nodes running intrusion detection independently.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
High performance intrusion detection using modified k mean & naïve bayeseSAT Journals
This document summarizes a research paper that proposes a hybrid intrusion detection system using modified k-means clustering and naive Bayes classification. The system aims to improve detection rates and reduce false alarms. It first uses modified k-means clustering to filter outliers and identify cluster centers in the data. It then uses naive Bayes classification to classify data points as normal or intrusions. The researchers tested their system on the KDD99 intrusion detection dataset and found it achieved high accuracy rates of over 99% with low false positive rates, outperforming other methods.
High performance intrusion detection using modified k mean & naïve bayeseSAT Journals
Abstract
Internet Technology is growing at exponential rate day by day, making data security of computer systems more complex and critical. There has been multiple methodology implemented for the same in recent time as detailed in [1], [3]. Availability of larger bandwidth has made the multiple large computer server network connected worldwide and thus increasing the load on the necessity to secure data and Intrusion detection system (IDS) is one of the most efficient technique to maintain security of computer system. The proposed system is designed in such a way that are helpful in identifying malicious behavior and improper use of computer system. In this report we proposed a hybrid technique for intrusion detection using data mining algorithms. Our main objective is to do complete analysis of intrusion detection Dataset to test the implemented system.In This report we will propose a new methodology in which Modified k-mean is used for clustering whereas Naïve Bayes for the classification. These two data mining techniques will be used for Intrusion detection in large horizontally distributed database.
Keywords: Intrusion Detection, Modified K-Mean, Naïve Bays
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Online Intrusion Alert Aggregation with Generative Data Stream ModelingIJMER
This document discusses online intrusion alert aggregation using generative data stream modeling. It proposes a probabilistic model to collect and model alerts of similar attacks over time to identify the beginning and completion of attacks. The system architecture includes sensor, detection, alert processing and reaction layers. The alert processing layer generates meta-alerts based on attack instance information. Experimental results show the approach can generate meta-alerts and reduce false positives by executing clustering algorithms multiple times on the data stream. It concludes the method is effective for aggregating alerts in real-time intrusion detection systems.
IRJET- Securing on Demand Source Routing Protocol in Mobile Ad-Hoc Networks b...IRJET Journal
This document discusses securing the Ad-Hoc On-Demand Distance Vector (AODV) routing protocol from wormhole attacks in mobile ad-hoc networks. It proposes using clustering to mitigate the effects of wormhole attacks. Wormhole attacks are powerful attacks in ad-hoc networks that involve tunneling network traffic between two colluding attackers. The document reviews related work on clustering algorithms, TCP and SYN cookies, security-enhanced RFID systems, wormhole detection algorithms, analyzing the impact of wormhole attacks, and new clustering protocols for mobile ad-hoc networks. It aims to simulate the effect of wormhole attacks on AODV and analyze network performance with and without such attacks using the NS2 network simulator.
1) The document proposes implementing an efficient K-means clustering algorithm to enhance connectivity and lifetime in wireless sensor networks.
2) It compares the proposed K-means algorithm to an existing Jumper Firefly algorithm based on energy consumption, network lifetime, and end-to-end delay.
3) Simulation results show the proposed K-means algorithm improves performance by reducing energy consumption from 16 to 12 Joules, increasing network lifetime by 96% compared to 83% for the existing algorithm, and lowering end-to-end delay from 3.7 to 2.7 seconds.
Efficient Secure Multi-Neuron Attack Defensive and Routing Security Technique...IRJET Journal
This document proposes a new secure routing technique for wireless mesh networks using unmanned aerial vehicles. The technique uses encryption algorithms like Data Encryption Standard and neural networks to establish a secure routing algorithm. It generates unique IDs for nodes, uses a key distribution center for authentication, and implements encryption to prevent attacks. Simulation results show the proposed approach reduces delay and improves packet delivery rate compared to existing routing protocols like PASER when there is frame error.
IRJET - Network Traffic Monitoring and Botnet Detection using K-ANN AlgorithmIRJET Journal
This document discusses a system for network traffic monitoring and botnet detection using the K-ANN (Kohonen Artificial Neural Network) algorithm. The system aims to address challenges in analyzing large amounts of network traffic data in real-time to accurately detect abnormal network activities and security threats. It involves collecting network packets using packet capture libraries, filtering the packets using K-ANN to detect botnet behavior, and notifying administrators of any detected threats. The system architecture and K-ANN algorithm are described. Results show the system was able to detect a SYN flood attack based on analyzing packet attributes. Future work will involve detecting additional types of network threats.
Implementation of Secured Network Based Intrusion Detection System Using SVM ...IRJET Journal
This document discusses the implementation of a secured network-based intrusion detection system using the support vector machine (SVM) algorithm. It begins with an abstract that outlines hardening different intrusion detection implementations and proposals. The paper then discusses using naive Bayes, a classification method for intrusion detection, to analyze transmitted data for malicious content and block transmissions from corrupted hosts. It also discusses using flow correlation information to improve classification accuracy while minimizing effects on network performance.
Similar to IRJET- A Secured Method of Data Aggregation for Wireless Sensor Networks in the Presence of Collusion Attacks (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
How to Manage Internal Notes in Odoo 17 POSCeline George
In this slide, we'll explore how to leverage internal notes within Odoo 17 POS to enhance communication and streamline operations. Internal notes provide a platform for staff to exchange crucial information regarding orders, customers, or specific tasks, all while remaining invisible to the customer. This fosters improved collaboration and ensures everyone on the team is on the same page.
A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
An Internet Protocol address (IP address) is a logical numeric address that is assigned to every single computer, printer, switch, router, tablets, smartphones or any other device that is part of a TCP/IP-based network.
Types of IP address-
Dynamic means "constantly changing “ .dynamic IP addresses aren't more powerful, but they can change.
Static means staying the same. Static. Stand. Stable. Yes, static IP addresses don't change.
Most IP addresses assigned today by Internet Service Providers are dynamic IP addresses. It's more cost effective for the ISP and you.
Online music portal management system project report.pdfKamal Acharya
The iMMS is a unique application that is synchronizing both user
experience and copyrights while providing services like online music
management, legal downloads, artists’ management. There are several
other applications available in the market that either provides some
specific services or large scale integrated solutions. Our product differs
from the rest in a way that we give more power to the users remaining
within the copyrights circle.
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
Profiling of Cafe Business in Talavera, Nueva Ecija: A Basis for Development ...IJAEMSJORNAL
This study aimed to profile the coffee shops in Talavera, Nueva Ecija, to develop a standardized checklist for aspiring entrepreneurs. The researchers surveyed 10 coffee shop owners in the municipality of Talavera. Through surveys, the researchers delved into the Owner's Demographic, Business details, Financial Requirements, and other requirements needed to consider starting up a coffee shop. Furthermore, through accurate analysis, the data obtained from the coffee shop owners are arranged to derive key insights. By analyzing this data, the study identifies best practices associated with start-up coffee shops’ profitability in Talavera. These findings were translated into a standardized checklist outlining essential procedures including the lists of equipment needed, financial requirements, and the Traditional and Social Media Marketing techniques. This standardized checklist served as a valuable tool for aspiring and existing coffee shop owners in Talavera, streamlining operations, ensuring consistency, and contributing to business success.
In May 2024, globally renowned natural diamond crafting company Shree Ramkrishna Exports Pvt. Ltd. (SRK) became the first company in the world to achieve GNFZ’s final net zero certification for existing buildings, for its two two flagship crafting facilities SRK House and SRK Empire. Initially targeting 2030 to reach net zero, SRK joined forces with the Global Network for Zero (GNFZ) to accelerate its target to 2024 — a trailblazing achievement toward emissions elimination.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!