AI neural networks can support disaster recovery and security operations in cloud computing systems. A neural network model is proposed that monitors a cloud computing network and can rebuild failed systems through new neural connections. The network uses cooperative coevolution algorithms and evolutionary algorithms to automate remediation. It involves distributed problem solving agents across the cloud network and a layered neural network collective that independently evaluates needs and repairs. This provides a robust, self-healing organizational model for cloud computing infrastructure and operations.
Compressing Neural Networks with Intel AI Lab's DistillerIntel Corporation
Learn about the many algorithms available for compressing Deep Neural Networks and how they are implemented in https://github.com/NervanaSystems/distiller.
You will get familiar with the main DNN compress concepts, terminology and algorithm classes.
NOTE: I've printed the original PowerPoint presentation as PDF with the speaker notes. It's not pretty, but this way you get to see the notes with the important references to the people who did all of this research work.
This document summarizes a research paper that proposes a new neural network algorithm called C-Mantec. C-Mantec adds competition between neurons using a thermal perceptron learning rule. This allows existing neurons to continue learning even as new neurons are added. The algorithm was tested on a diabetes dataset and shown to generate compact neural network architectures with good generalization capabilities. It was implemented in an FPGA using Xilinx ISE for synthesis and ModelSim for simulation. The output was obtained over three clock cycles, first loading inputs/weights, then multiplying/accumulating weights, and finally checking the output against a threshold. The study showed C-Mantec can effectively model glucose level fluctuations to determine if a patient has diabetes.
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...IJCNCJournal
The document describes a proposed secure clustering protocol for wireless sensor networks that aims to reduce energy consumption while satisfying security needs. The protocol has four phases: 1) Preparation, where keys are distributed and dynamic clusters are formed based on signal strength. Nodes send identifiers to the base station. 2) Aggregator selection, where a node is selected in each cluster as the data aggregator. 3) Data aggregation, where non-aggregator nodes send data to the aggregator node. 4) Data gathering, where aggregator nodes send aggregated data to the base station. The goal is to distribute workload, reduce transmissions, and hide the identities of aggregator nodes to protect against attacks.
Cluster-based Target Tracking and Recovery Algorithm in Wireless Sensor NetworkIJASCSE
This document summarizes a research paper that proposes an energy efficient recovery technique for target tracking in wireless sensor networks. It begins with an introduction to target tracking in WSNs and the related challenges of energy efficiency and recovery when targets are lost. It then reviews existing target tracking and recovery techniques. The proposed technique uses dynamic clustering to predict target locations and only wakes clusters near the predicted path to save energy. The recovery mechanism has three steps: 1) the current cluster head announces a lost target, 2) it initiates recovery by waking nearby clusters to search predicted locations, and 3) once the target is found, only tracking clusters remain active while others sleep. The paper argues this approach reduces communication overhead and active clusters compared to existing
Key Management Schemes for Secure Communication in Heterogeneous Sensor NetworksIDES Editor
Hierarchical Sensor Network organization is
widely used to achieve energy efficiency in Wireless Sensor
Networks(WSN). To achieve security in hierarchical WSN,
it is important to be able to encrypt the messages sent
between sensor nodes and its cluster head. The key
management task is challenging due to resource constrained
nature of WSN. In this paper we are proposing two key
management schemes for hierarchical networks which
handles various events like node addition, node compromise
and key refresh at regular intervals. The Tree-Based
Scheme ensures in-network processing by maintaining some
additional intermediate keys. Whereas the CRT-Based
Scheme performs the key management with minimum
communication and storage at each node.
This document discusses setting up a homogeneous Beowulf cluster using commodity computers and the MPI (Message Passing Interface). Specifically:
1. It focuses on formulating simpler ways to create a private, high-performance Beowulf cluster environment where nodes collectively execute large tasks in parallel processes distributed across nodes.
2. The cluster setup here uses the MPICH2 message passing library. The output shows the process IDs and hostnames of processes running on each node.
3. A Beowulf cluster is described as a centralized master node coordinating a set of interconnected commodity computers to provide high-performance computing through task distribution and parallel processing across nodes.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
This paper proposes improvements to the Chord peer-to-peer network protocol to increase reliability of data transfer. The Chord protocol uses a ring topology to organize nodes and determine routing, but direct routes sometimes fail. The paper suggests having nodes store failed packets locally and route them through multiple neighboring nodes to reach the destination. It also considers available bandwidth when selecting next-hop nodes. Simulations show the proposed methods increase packet delivery ratio compared to the base Chord protocol, especially as node movement increases, improving the reliability of data transfer in the peer-to-peer network.
Compressing Neural Networks with Intel AI Lab's DistillerIntel Corporation
Learn about the many algorithms available for compressing Deep Neural Networks and how they are implemented in https://github.com/NervanaSystems/distiller.
You will get familiar with the main DNN compress concepts, terminology and algorithm classes.
NOTE: I've printed the original PowerPoint presentation as PDF with the speaker notes. It's not pretty, but this way you get to see the notes with the important references to the people who did all of this research work.
This document summarizes a research paper that proposes a new neural network algorithm called C-Mantec. C-Mantec adds competition between neurons using a thermal perceptron learning rule. This allows existing neurons to continue learning even as new neurons are added. The algorithm was tested on a diabetes dataset and shown to generate compact neural network architectures with good generalization capabilities. It was implemented in an FPGA using Xilinx ISE for synthesis and ModelSim for simulation. The output was obtained over three clock cycles, first loading inputs/weights, then multiplying/accumulating weights, and finally checking the output against a threshold. The study showed C-Mantec can effectively model glucose level fluctuations to determine if a patient has diabetes.
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...IJCNCJournal
The document describes a proposed secure clustering protocol for wireless sensor networks that aims to reduce energy consumption while satisfying security needs. The protocol has four phases: 1) Preparation, where keys are distributed and dynamic clusters are formed based on signal strength. Nodes send identifiers to the base station. 2) Aggregator selection, where a node is selected in each cluster as the data aggregator. 3) Data aggregation, where non-aggregator nodes send data to the aggregator node. 4) Data gathering, where aggregator nodes send aggregated data to the base station. The goal is to distribute workload, reduce transmissions, and hide the identities of aggregator nodes to protect against attacks.
Cluster-based Target Tracking and Recovery Algorithm in Wireless Sensor NetworkIJASCSE
This document summarizes a research paper that proposes an energy efficient recovery technique for target tracking in wireless sensor networks. It begins with an introduction to target tracking in WSNs and the related challenges of energy efficiency and recovery when targets are lost. It then reviews existing target tracking and recovery techniques. The proposed technique uses dynamic clustering to predict target locations and only wakes clusters near the predicted path to save energy. The recovery mechanism has three steps: 1) the current cluster head announces a lost target, 2) it initiates recovery by waking nearby clusters to search predicted locations, and 3) once the target is found, only tracking clusters remain active while others sleep. The paper argues this approach reduces communication overhead and active clusters compared to existing
Key Management Schemes for Secure Communication in Heterogeneous Sensor NetworksIDES Editor
Hierarchical Sensor Network organization is
widely used to achieve energy efficiency in Wireless Sensor
Networks(WSN). To achieve security in hierarchical WSN,
it is important to be able to encrypt the messages sent
between sensor nodes and its cluster head. The key
management task is challenging due to resource constrained
nature of WSN. In this paper we are proposing two key
management schemes for hierarchical networks which
handles various events like node addition, node compromise
and key refresh at regular intervals. The Tree-Based
Scheme ensures in-network processing by maintaining some
additional intermediate keys. Whereas the CRT-Based
Scheme performs the key management with minimum
communication and storage at each node.
This document discusses setting up a homogeneous Beowulf cluster using commodity computers and the MPI (Message Passing Interface). Specifically:
1. It focuses on formulating simpler ways to create a private, high-performance Beowulf cluster environment where nodes collectively execute large tasks in parallel processes distributed across nodes.
2. The cluster setup here uses the MPICH2 message passing library. The output shows the process IDs and hostnames of processes running on each node.
3. A Beowulf cluster is described as a centralized master node coordinating a set of interconnected commodity computers to provide high-performance computing through task distribution and parallel processing across nodes.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
This paper proposes improvements to the Chord peer-to-peer network protocol to increase reliability of data transfer. The Chord protocol uses a ring topology to organize nodes and determine routing, but direct routes sometimes fail. The paper suggests having nodes store failed packets locally and route them through multiple neighboring nodes to reach the destination. It also considers available bandwidth when selecting next-hop nodes. Simulations show the proposed methods increase packet delivery ratio compared to the base Chord protocol, especially as node movement increases, improving the reliability of data transfer in the peer-to-peer network.
Neural Network Based Individual Classification SystemIRJET Journal
This document describes a neural network model developed for individual classification. The model was designed to measure personality traits through a questionnaire. It then uses a neural network trained on sample data through unsupervised and supervised learning with multi-layer perceptions. The backpropagation algorithm was used to train the network. The neural network architecture included multiple neuron layers trained on a 200 item data set, achieving 99.82% accuracy. The goal was to classify individuals into high, middle, and low personality categories for use in job selection or training.
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...csandit
Data Aggregation is a vital aspect in WSNs (Wireless Sensor Networks) and this is because it
reduces the quantity of data to be transmitted over the complex network. In earlier studies
authors used homomorphic encryption properties for concealing statement during aggregation
such that encrypted data can be aggregated algebraically without decrypting them. These
schemes are not applicable for multi applications which lead to proposal of Concealed Data
Aggregation for Multi Applications (CDAMA). It is designed for multi applications, as it
provides secure counting ability. In wireless sensor networks SN are unarmed and are
susceptible to attacks. Considering the defence aspect of wireless environment we have used
DYDOG (Dynamic Intrusion Detection Protocol Model) and a customized key generation
procedure that uses Digital Signatures and also Two Fish Algorithms along with CDAMA for
augmentation of security and throughput. To prove our proposed scheme’s robustness and
effectiveness, we conducted the simulations, inclusive analysis and comparisons at the ending.
Network clustering is an important technique used in many large-scale distributed systems. Given good design and implementation, network clustering can significantly enhance the system\'s scalability and efficiency. However, it is very challenging to design a good clustering protocol for networks that scale fast and change continuously. In this paper, we propose a distributed network clustering protocol SDC targeting large-scale decentralized systems. In SDC, clusters are dynamically formed and adjusted based on SCM, a practical clustering accuracy measure. Based on SCM, each node can join or leave a cluster such that the clustering accuracy of the whole network can be improved. A big advantage of SDC is it can recover accurate clusters from node dynamics with very small message overhead. Through extensive simulations, we conclude that SDC is able to discover good quality clusters very efficiently.
A Traffic-Aware Key Management Architecture for Reducing Energy Consumption i...IDES Editor
In Wireless Sensor Networks (WSNs), most
of the existing key management schemes, establish shared
keys for all pairs of neighbor sensor nodes without
considering the communication between these nodes.
When the number of sensor nodes in WSNs is increased
then each sensor node is to be loaded with bulky amount
of keys. In WSNs a sensor node may communicate with a
small set of neighbor sensor nodes. Based on this fact, in
this paper, an energy efficient Traffic-Aware Key
Management (TKM) scheme is developed for WSNs,
which only establishes shared keys for active sensors
which participate in direct communication. The proposed
scheme offers an efficient Re-keying mechanism to
broadcast keys without the need for retransmission or
acknowledgements. Numerical results show that proposed
key management scheme achieves high connectivity. In
the simulation experiments, the proposed key
management scheme is applied for different routing
protocols. The performance evaluation shows that
proposed scheme gives stronger resilence, low energy
consumption and lesser end to end delay.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
The Design A Fuzzy System in Order to Schedule Sleep and Waking Of Sensors in...IJERA Editor
Sensor networks are considered to be a standard technology in wireless communications and they are widely
used in military, surveillance, medicine, industry and houses as well. In sensor networks batteries with limited
amount of energy provide energy for the whole system. They are not rechargeable and as soon as the batteries
die the network life time will expire too. Using computational intelligence to schedule sleep and waking of the
sensor nodes is one of the suitable methods which helps the network to have a longer life. In this paper the focus
is on a fuzzy method to schedule sleeping and waking of sensor nodes. In this method the Environmental
conditions of each sensor (the number of neighbors, the remaining energy, and the distance to the next cluster
node) are considered as inputs by the application of a fuzzy system based on which the system creates an output
and the sleeping and waking time of each sensor is dynamically determined. The simulated results show that the
proposed algorithm is more efficient than other basic methods and consume less energy as well.
Securing Privacy of User’s Data on Cloud Using Back Propagation Neural Networkstheijes
To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Backpropagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily portioned data set, to collaboratively conduct the learning. this paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. To support flexible operations over ciphertexts, we adopt and tailor the BGN ‘doubly homomorphic’ encryption algorithm for the multi-party setting..
This document proposes an approach for intrusion detection that uses clustering and classification. In the first phase, the K-means clustering algorithm is used to generate clusters of similar attack types from network/system activity data streams. The centroids generated by K-means are then used in the second classification phase, where the K-nearest neighbors and decision tree algorithms detect the different types of attacks present in the data. The overall goal is to take advantage of clustering to group similar attacks first before classifying them, which can provide better detection performance than using a single classifier alone.
Coupling-Based Internal Clock Synchronization for Large Scale Dynamic Distrib...Angelo Corsaro
This paper studies the problem of realizing a common software clock among a large set of nodes without an external time reference (i.e., internal clock synchronization), any centralized control and where nodes can join and leave the distributed system at their will. The paper proposes an internal clock synchronization algorithm which combines the gossip-based paradigm with a nature-inspired approach, coming from the coupled oscillators phenomenon, to cope with scale and churn. The algorithm works on the top of an overlay network and uses a uniform peer sampling service to fullfill each node’s local view. Therefore, differently from clock synchronization protocols for small scale and static distributed systems, here each node synchronizes regularly with only the neighbors in its local view and not with the whole system. Theoretical and empirical evaluations of the convergence speed and of the synchronization error of the coupled-based internal clock synchronization algorithm have been carried out, showing how convergence time and the synchronization error depends on the coupling factor and on the local view size. Moreover the variation of the synchronization error with respect to churn and the impact of a sudden variation of the number of nodes have been analyzed to show the stability of the algorithm. In all these contexts, the algorithm shows nice performance and very good self-organizing properties. Finally, we showed how the assumption on the existence of a uniform peer-sampling service is instrumental for the good behavior of the algorithm.
This document summarizes a research paper that analyzes and compares the energy consumption of different key management schemes in wireless sensor networks (WSNs). It finds that the NchooseK key management scheme consumes less energy than the pre-shared key (PSK) scheme for various key operations like generation, distribution, encryption, and verification. Through simulations of WSNs of varying sizes, the paper measures the energy used by each scheme during different steps and operations. The results show that NchooseK has more stable and consistent energy patterns compared to PSK, and consumes less energy overall for authentication and communication between sensor nodes. The paper concludes that NchooseK is a more scalable and energy-efficient scheme for key management in W
Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Net...IJASCSE
This document proposes a Clustering Based Lifetime Maximizing Aggregation Tree (CLMAT) algorithm for wireless sensor networks. The algorithm aims to reduce energy consumption by creating an aggregation tree that minimizes distance traversed, energy consumed, and cost. It considers the three factors of energy, distance, and cost simultaneously when constructing the tree, unlike previous works. The tree is structured to maximize network lifetime by selecting nodes with higher residual energy as parents where possible. Pseudocode is provided to generate the aggregation tree using clustering by calculating branch and tree energy, distance, and cost at each step to ultimately select the tree with the highest lifetime, lowest energy consumption, distance, and cost.
A survey on Object Tracking Techniques in Wireless Sensor NetworkIRJET Journal
This document summarizes various object tracking techniques in wireless sensor networks. It begins with an introduction to wireless sensor networks and object tracking applications. It then classifies network architectures for object tracking into four categories: naive architecture, tree-based architecture, cluster-based architecture, and hybrid architecture. For each category, several representative algorithms are described in terms of how they perform object detection, data transmission, energy efficiency, and other metrics. Overall, most algorithms aim to minimize energy consumption by activating only a subset of sensor nodes for tracking and using techniques like clustering, prediction, and dynamic scheduling of active sensor nodes.
IMPLEMENTATION OF SECURITY PROTOCOL FOR WIRELESS SENSORijcsa
Intrusion Detection is one of the methods of defending against these attacks. In the proposed a security protocol for homogeneous wireless sensor network; network with all nodes are of same type. Clustering is used to improve the energy efficiency. Zone-Based Cluster Protocol (ZBCA) is used for selection of cluster head which is effective in scalability and energy consumption. Single hop technique is used for
communication within normal nodes and cluster head to base station. Simulation of proposed algorithm is performed in MATLAB. Sleep Deprivation Attack has been analyzed where attacker changes the environmental values by an artificial event. Attacker produces an event in environment due to which nodes have to sense the environment more than once in the same round that increase the power consumption of
the node. This interrupt reduces the network life time as nodes are not allowed to go in sleep mode and they are not able to perform their function of data collection and reporting to Cluster head and Base Station properly. Proposed protocol identifies this attack and prevents it from happening by solating the attacker node.
Secure data aggregation technique for wireless sensor networks in the presenc...LogicMindtech Nologies
This document discusses secure data aggregation techniques for wireless sensor networks in the presence of collusion attacks. It describes how existing iterative filtering algorithms used for data aggregation and trust assessment in sensor networks are vulnerable to sophisticated collusion attacks. The proposed solution improves iterative filtering techniques by providing an initial trust estimate based on robust estimation of individual sensor errors. This makes the algorithms robust against collusion attacks while also making them more accurate and faster converging.
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.
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
Neural networks are a new method of programming computers that are good at pattern recognition. They are inspired by the human brain and are composed of interconnected processing elements called neurons. Neural networks learn by example through adjusting synaptic connections between neurons. They can be trained to perform tasks like pattern recognition and classification. There are different types of neural networks including feedforward and feedback networks. Training involves adjusting weights to minimize error through algorithms like backpropagation. Neural networks are used in applications like data analysis, forecasting, and medical diagnosis.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
A Novel Approach for Detection of Routes with Misbehaving Nodes in MANETsIDES Editor
Network nodes in MANET’s are free to move randomly.
Therefore, the network topology may change rapidly.
Routing protocol for MANET’s are used for delivery of data
packets from source to the desired destination, Routing protocols
are also designed based on the assumption that all the
participating nodes are fully cooperative. However, due to the
scarcely available battery based energy, node behaviours may
exist. One such routing misbehaviours is that some nodes may
be selfish by participating in route discovery and maintenance
process, but refuse to forward the packet in order to save its
energy. To solve this problem we propose a reputation based
scheme where the watch dog uses a passive overhearing of
nodes and assign a value to it as an appreciation or add nuggets
to them. In this proposal, nodes with highest value are
highly recommended for data forwarding and allow nodes to
avoid the use of misbehaving nodes in future route selection.
AdHoc On Demand Distance vector routing protocol may be
used to get the recommendation details of the node intended
to forward the packet from the neighbouring nodes. This paper
proposes a novel method to mitigate the route with misbehaving
nodes and also suggests a way to find if any intruder is
present in the cluster of participating nodes using security
aware AODV protocol.
Energy consumption mitigation__routing_protocols_for_large_wsn's_finalGr Patel
This document summarizes and compares four routing protocols for large-scale wireless sensor networks that aim to mitigate energy consumption:
1. Data Gathering algorithm based on Mobile Agent (DGMA) proposes dynamic clustering based on event severity and uses a mobile agent to collect data from cluster members to reduce energy consumption and network delay.
2. Dynamic Minimal Spanning Tree Routing Protocol (DMSTRP) is a cluster-based protocol that uses minimal spanning trees to connect nodes within and between clusters to more efficiently reduce transmission distances and energy usage compared to other protocols.
3. Hierarchical Geographic Multicast Routing (HGMR) decomposes multicast groups hierarchically and uses geographic routing within subgroups to simultaneously achieve
Handwritten Digit Recognition using Convolutional Neural NetworksIRJET Journal
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
Neural Network Based Individual Classification SystemIRJET Journal
This document describes a neural network model developed for individual classification. The model was designed to measure personality traits through a questionnaire. It then uses a neural network trained on sample data through unsupervised and supervised learning with multi-layer perceptions. The backpropagation algorithm was used to train the network. The neural network architecture included multiple neuron layers trained on a 200 item data set, achieving 99.82% accuracy. The goal was to classify individuals into high, middle, and low personality categories for use in job selection or training.
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...csandit
Data Aggregation is a vital aspect in WSNs (Wireless Sensor Networks) and this is because it
reduces the quantity of data to be transmitted over the complex network. In earlier studies
authors used homomorphic encryption properties for concealing statement during aggregation
such that encrypted data can be aggregated algebraically without decrypting them. These
schemes are not applicable for multi applications which lead to proposal of Concealed Data
Aggregation for Multi Applications (CDAMA). It is designed for multi applications, as it
provides secure counting ability. In wireless sensor networks SN are unarmed and are
susceptible to attacks. Considering the defence aspect of wireless environment we have used
DYDOG (Dynamic Intrusion Detection Protocol Model) and a customized key generation
procedure that uses Digital Signatures and also Two Fish Algorithms along with CDAMA for
augmentation of security and throughput. To prove our proposed scheme’s robustness and
effectiveness, we conducted the simulations, inclusive analysis and comparisons at the ending.
Network clustering is an important technique used in many large-scale distributed systems. Given good design and implementation, network clustering can significantly enhance the system\'s scalability and efficiency. However, it is very challenging to design a good clustering protocol for networks that scale fast and change continuously. In this paper, we propose a distributed network clustering protocol SDC targeting large-scale decentralized systems. In SDC, clusters are dynamically formed and adjusted based on SCM, a practical clustering accuracy measure. Based on SCM, each node can join or leave a cluster such that the clustering accuracy of the whole network can be improved. A big advantage of SDC is it can recover accurate clusters from node dynamics with very small message overhead. Through extensive simulations, we conclude that SDC is able to discover good quality clusters very efficiently.
A Traffic-Aware Key Management Architecture for Reducing Energy Consumption i...IDES Editor
In Wireless Sensor Networks (WSNs), most
of the existing key management schemes, establish shared
keys for all pairs of neighbor sensor nodes without
considering the communication between these nodes.
When the number of sensor nodes in WSNs is increased
then each sensor node is to be loaded with bulky amount
of keys. In WSNs a sensor node may communicate with a
small set of neighbor sensor nodes. Based on this fact, in
this paper, an energy efficient Traffic-Aware Key
Management (TKM) scheme is developed for WSNs,
which only establishes shared keys for active sensors
which participate in direct communication. The proposed
scheme offers an efficient Re-keying mechanism to
broadcast keys without the need for retransmission or
acknowledgements. Numerical results show that proposed
key management scheme achieves high connectivity. In
the simulation experiments, the proposed key
management scheme is applied for different routing
protocols. The performance evaluation shows that
proposed scheme gives stronger resilence, low energy
consumption and lesser end to end delay.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
The Design A Fuzzy System in Order to Schedule Sleep and Waking Of Sensors in...IJERA Editor
Sensor networks are considered to be a standard technology in wireless communications and they are widely
used in military, surveillance, medicine, industry and houses as well. In sensor networks batteries with limited
amount of energy provide energy for the whole system. They are not rechargeable and as soon as the batteries
die the network life time will expire too. Using computational intelligence to schedule sleep and waking of the
sensor nodes is one of the suitable methods which helps the network to have a longer life. In this paper the focus
is on a fuzzy method to schedule sleeping and waking of sensor nodes. In this method the Environmental
conditions of each sensor (the number of neighbors, the remaining energy, and the distance to the next cluster
node) are considered as inputs by the application of a fuzzy system based on which the system creates an output
and the sleeping and waking time of each sensor is dynamically determined. The simulated results show that the
proposed algorithm is more efficient than other basic methods and consume less energy as well.
Securing Privacy of User’s Data on Cloud Using Back Propagation Neural Networkstheijes
To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Backpropagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily portioned data set, to collaboratively conduct the learning. this paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. To support flexible operations over ciphertexts, we adopt and tailor the BGN ‘doubly homomorphic’ encryption algorithm for the multi-party setting..
This document proposes an approach for intrusion detection that uses clustering and classification. In the first phase, the K-means clustering algorithm is used to generate clusters of similar attack types from network/system activity data streams. The centroids generated by K-means are then used in the second classification phase, where the K-nearest neighbors and decision tree algorithms detect the different types of attacks present in the data. The overall goal is to take advantage of clustering to group similar attacks first before classifying them, which can provide better detection performance than using a single classifier alone.
Coupling-Based Internal Clock Synchronization for Large Scale Dynamic Distrib...Angelo Corsaro
This paper studies the problem of realizing a common software clock among a large set of nodes without an external time reference (i.e., internal clock synchronization), any centralized control and where nodes can join and leave the distributed system at their will. The paper proposes an internal clock synchronization algorithm which combines the gossip-based paradigm with a nature-inspired approach, coming from the coupled oscillators phenomenon, to cope with scale and churn. The algorithm works on the top of an overlay network and uses a uniform peer sampling service to fullfill each node’s local view. Therefore, differently from clock synchronization protocols for small scale and static distributed systems, here each node synchronizes regularly with only the neighbors in its local view and not with the whole system. Theoretical and empirical evaluations of the convergence speed and of the synchronization error of the coupled-based internal clock synchronization algorithm have been carried out, showing how convergence time and the synchronization error depends on the coupling factor and on the local view size. Moreover the variation of the synchronization error with respect to churn and the impact of a sudden variation of the number of nodes have been analyzed to show the stability of the algorithm. In all these contexts, the algorithm shows nice performance and very good self-organizing properties. Finally, we showed how the assumption on the existence of a uniform peer-sampling service is instrumental for the good behavior of the algorithm.
This document summarizes a research paper that analyzes and compares the energy consumption of different key management schemes in wireless sensor networks (WSNs). It finds that the NchooseK key management scheme consumes less energy than the pre-shared key (PSK) scheme for various key operations like generation, distribution, encryption, and verification. Through simulations of WSNs of varying sizes, the paper measures the energy used by each scheme during different steps and operations. The results show that NchooseK has more stable and consistent energy patterns compared to PSK, and consumes less energy overall for authentication and communication between sensor nodes. The paper concludes that NchooseK is a more scalable and energy-efficient scheme for key management in W
Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Net...IJASCSE
This document proposes a Clustering Based Lifetime Maximizing Aggregation Tree (CLMAT) algorithm for wireless sensor networks. The algorithm aims to reduce energy consumption by creating an aggregation tree that minimizes distance traversed, energy consumed, and cost. It considers the three factors of energy, distance, and cost simultaneously when constructing the tree, unlike previous works. The tree is structured to maximize network lifetime by selecting nodes with higher residual energy as parents where possible. Pseudocode is provided to generate the aggregation tree using clustering by calculating branch and tree energy, distance, and cost at each step to ultimately select the tree with the highest lifetime, lowest energy consumption, distance, and cost.
A survey on Object Tracking Techniques in Wireless Sensor NetworkIRJET Journal
This document summarizes various object tracking techniques in wireless sensor networks. It begins with an introduction to wireless sensor networks and object tracking applications. It then classifies network architectures for object tracking into four categories: naive architecture, tree-based architecture, cluster-based architecture, and hybrid architecture. For each category, several representative algorithms are described in terms of how they perform object detection, data transmission, energy efficiency, and other metrics. Overall, most algorithms aim to minimize energy consumption by activating only a subset of sensor nodes for tracking and using techniques like clustering, prediction, and dynamic scheduling of active sensor nodes.
IMPLEMENTATION OF SECURITY PROTOCOL FOR WIRELESS SENSORijcsa
Intrusion Detection is one of the methods of defending against these attacks. In the proposed a security protocol for homogeneous wireless sensor network; network with all nodes are of same type. Clustering is used to improve the energy efficiency. Zone-Based Cluster Protocol (ZBCA) is used for selection of cluster head which is effective in scalability and energy consumption. Single hop technique is used for
communication within normal nodes and cluster head to base station. Simulation of proposed algorithm is performed in MATLAB. Sleep Deprivation Attack has been analyzed where attacker changes the environmental values by an artificial event. Attacker produces an event in environment due to which nodes have to sense the environment more than once in the same round that increase the power consumption of
the node. This interrupt reduces the network life time as nodes are not allowed to go in sleep mode and they are not able to perform their function of data collection and reporting to Cluster head and Base Station properly. Proposed protocol identifies this attack and prevents it from happening by solating the attacker node.
Secure data aggregation technique for wireless sensor networks in the presenc...LogicMindtech Nologies
This document discusses secure data aggregation techniques for wireless sensor networks in the presence of collusion attacks. It describes how existing iterative filtering algorithms used for data aggregation and trust assessment in sensor networks are vulnerable to sophisticated collusion attacks. The proposed solution improves iterative filtering techniques by providing an initial trust estimate based on robust estimation of individual sensor errors. This makes the algorithms robust against collusion attacks while also making them more accurate and faster converging.
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.
Energy Efficient Multipath Data Fusion Technique for Wireless Sensor NetworksIDES Editor
In wireless sensor networks (WSN), data fusion
should be energy efficient. But, determining the optimal
number of aggregators in an energy efficient manner is a
challenging task. Moreover, the existing data fusion
techniques mostly use the same path for transmitting
aggregated data to the sink which reduces the nodes lifetime.
In this paper, we propose a technique which combines energy
efficiency and multiple path selection for data fusion in WSN.
The network is partitioned into various clusters and the node
with highest residual energy is selected as the cluster head.
The sink computes multiple paths to each cluster head for
data transmission. The distributed source coding and the
lifting scheme wavelet transform are used for compressing
the data at the CH. During each round of transmission, the
path is changed in a round robin manner, to conserve the
energy. This process is repeated for each cluster. From our
simulation results we show that this data fusion technique
has less energy consumption with increased packet delivery
ratio, when compared with the existing schemes.
Neural networks are a new method of programming computers that are good at pattern recognition. They are inspired by the human brain and are composed of interconnected processing elements called neurons. Neural networks learn by example through adjusting synaptic connections between neurons. They can be trained to perform tasks like pattern recognition and classification. There are different types of neural networks including feedforward and feedback networks. Training involves adjusting weights to minimize error through algorithms like backpropagation. Neural networks are used in applications like data analysis, forecasting, and medical diagnosis.
Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Eff...CSCJournals
The objective of this paper is to develop a mechanism to increase the lifetime of homogeneous wireless sensor networks (WSNs) through minimizing long range communication, efficient data delivery and energy balancing. Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. In cluster based architecture, the role of aggregator node is very crucial because of extra processing and long range communication. Once the aggregator node becomes non functional, it affects the whole cluster. We introduced a candidate cluster head node on the basis of node density. We proposed a modified cluster based WSN architecture by introducing a server node (SN) that is rich in terms of resources. This server node (SN) takes the responsibility of transmitting data to the base station over longer distances from the cluster head. We proposed cluster head selection algorithm based on residual energy, distance, reliability and degree of mobility. The proposed method can save overall energy consumption and extend the lifetime of the sensor network and also addresses robustness against even/uneven node deployment.
A Novel Approach for Detection of Routes with Misbehaving Nodes in MANETsIDES Editor
Network nodes in MANET’s are free to move randomly.
Therefore, the network topology may change rapidly.
Routing protocol for MANET’s are used for delivery of data
packets from source to the desired destination, Routing protocols
are also designed based on the assumption that all the
participating nodes are fully cooperative. However, due to the
scarcely available battery based energy, node behaviours may
exist. One such routing misbehaviours is that some nodes may
be selfish by participating in route discovery and maintenance
process, but refuse to forward the packet in order to save its
energy. To solve this problem we propose a reputation based
scheme where the watch dog uses a passive overhearing of
nodes and assign a value to it as an appreciation or add nuggets
to them. In this proposal, nodes with highest value are
highly recommended for data forwarding and allow nodes to
avoid the use of misbehaving nodes in future route selection.
AdHoc On Demand Distance vector routing protocol may be
used to get the recommendation details of the node intended
to forward the packet from the neighbouring nodes. This paper
proposes a novel method to mitigate the route with misbehaving
nodes and also suggests a way to find if any intruder is
present in the cluster of participating nodes using security
aware AODV protocol.
Energy consumption mitigation__routing_protocols_for_large_wsn's_finalGr Patel
This document summarizes and compares four routing protocols for large-scale wireless sensor networks that aim to mitigate energy consumption:
1. Data Gathering algorithm based on Mobile Agent (DGMA) proposes dynamic clustering based on event severity and uses a mobile agent to collect data from cluster members to reduce energy consumption and network delay.
2. Dynamic Minimal Spanning Tree Routing Protocol (DMSTRP) is a cluster-based protocol that uses minimal spanning trees to connect nodes within and between clusters to more efficiently reduce transmission distances and energy usage compared to other protocols.
3. Hierarchical Geographic Multicast Routing (HGMR) decomposes multicast groups hierarchically and uses geographic routing within subgroups to simultaneously achieve
Handwritten Digit Recognition using Convolutional Neural NetworksIRJET Journal
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
ACCELERATED DEEP LEARNING INFERENCE FROM CONSTRAINED EMBEDDED DEVICESIAEME Publication
Hardware looping is a feature of some processor instruction sets whose hardware can repeat the body of a loop automatically, rather than requiring software instructions which take up cycles (and therefore time) to do so. Loop Unrolling is a loop transformation technique that attempts to advance a program's execution speed to the detriment of its twofold size, which is a methodology known as space–time tradeoff. A convolutional neural network is created with simple loops, with hardware looping, with loop unrolling and with both hardware looping and loop unrolling, and a comparison is made to evaluate the effectiveness of hardware looping and loop unrolling. The hardware loops alone will add to a cycle check decline, while the mix of hardware loops and dot product instructions will decrease the clock cycle tally further. The CNN is simulated on Xilinx Vivado 2021.1 running on Zync-7000 FPGA.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
IRJET- The Essentials of Neural Networks and their ApplicationsIRJET Journal
This document discusses neural networks and their applications. It begins by explaining the limitations of traditional computers in complex tasks and the need for a more human-like approach using neural networks. The basics of neural network architecture are then described, including the key components of neurons and synapses that operate in parallel like the human brain. Different types of neural networks are classified, including convolutional neural networks for image data. The document concludes by highlighting the wide range of commercial applications for neural networks in areas like data analysis, forecasting, and military operations.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE_ Neural Networks.pptxJavier Daza
The document discusses reactive systems and neural networks. It defines reactive systems as software that rapidly responds to environmental events and changes. Neural networks are artificial intelligence models inspired by the human brain that consist of interconnected artificial neurons. They are used for tasks like machine learning, pattern recognition, and complex data processing. The document then goes into more detail about the structure of artificial neural networks and the components and functioning of artificial neurons.
Artificial Neural Network: A brief studyIRJET Journal
This document provides an overview of artificial neural networks (ANN), including:
- ANNs are computational models inspired by the human brain that are designed to analyze and draw conclusions from experiences. They contain interconnected nodes that work together to solve problems.
- The key components of an ANN include an input layer, one or more hidden layers, and an output layer. Data is fed into the input layer and passes through the hidden layers before emerging as output.
- ANNs can be trained to learn from large datasets using supervised, unsupervised, or reinforcement learning techniques. The weights between nodes are adjusted during training to minimize error between the network's predictions and correct outputs.
- Once trained, ANNs can
Implementation of Feed Forward Neural Network for Classification by Education...ijsrd.com
in the last few years, the electronic devices production field has witness a great revolution by having the new birth of the extraordinary FPGA (Field Programmable Gate Array) family platforms. These platforms are the optimum and best choice for the modern digital systems now a day. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. In this paper a hardware design of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. Digital system architecture is designed to realize a feed forward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL).General Terms-Network.
This document summarizes a senior design project that implemented a perceptron neural network using Verilog and synthesized it on an FPGA board. Key aspects include:
1) The project developed modules for a perceptron neuron using a custom floating-point numbering system with 32-bit words. This included multiplier and adder modules.
2) The perceptron was trained using a linear activation function to classify linearly separable data. Modules were created to perform the input, activation, and weight update functions.
3) Modules were simulated in ModelSim and synthesized on an Altera DE2-115 FPGA board. The design followed an iterative process to refine requirements as understanding improved.
A simplified design of multiplier for multi layer feed forward hardware neura...eSAT Publishing House
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
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.
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
This document proposes a grouped grid node model to improve resource allocation in highly dynamic grid environments. It aims to reduce CPU consumption caused by nodes monitoring each other by classifying nodes into groups based on CPU specifications. Each group will have a group-agent node that handles monitoring and resource allocation for that group, rather than every node monitoring every other node. The proposed model includes user interface, group, resource, and worker modules to classify nodes, exchange group information, manage node resources, and execute jobs. It is argued that this grouped approach will more efficiently allocate resources as the scale of the grid increases compared to previous non-grouped models.
The document summarizes Daniele Ciriello's master's thesis on using convolutional neural networks for computer vision tasks. It introduces computer vision and neural networks concepts. It then discusses implementing residual neural networks to classify images from CIFAR-10, MNIST, and a distracted driver detection dataset. The results showed high accuracy rates of 90.41%, 99.64%, and 99.75% respectively, demonstrating that residual networks can effectively solve computer vision problems. Saliency maps were also used to visualize what image regions the networks focused on for classifications.
CAMP: cluster aided multi-path routing protocol for the wireless sensor, according to an article written by "Mohit Sajwan1 • Devashish Gosain2 • Ajay K. Sharma1
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.
Neural networks in business forecastingAmir Shokri
The document discusses the use of neural networks for business forecasting. It begins with an abstract and introduction about neural networks and their applications. It then provides details on how neural networks work, including their structure of interconnected nodes that learn patterns from data. The document discusses how neural networks can be used as a forecasting tool and provides examples of their use in areas like marketing and finance. It also provides a table listing other business forecasting applications reported in literature. The conclusion emphasizes that neural networks have been successfully used for business forecasting but that each application requires careful modeling and consideration of issues. No single method like neural networks is always best, and combining methods may improve performance.
Artificial Neural Network Implementation On FPGA ChipMaria Perkins
This document discusses implementing artificial neural networks on field programmable gate arrays (FPGAs). It begins with an abstract noting ANNs have been mostly implemented in software, but hardware versions can provide better performance. The document then reviews work done by various researchers on implementing ANNs using FPGAs. It discusses the structure of artificial neurons and feedforward neural networks. It also provides an overview of VHDL and describes different architectures for implementing artificial neurons in hardware using FPGAs. The goal is to help young researchers in implementing and realizing ANNs with hardware.
Similar to IRJET-AI Neural Network Disaster Recovery Cloud Operations Systems (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.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.