Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. Bidirectional LSTM (BiLSTM) networks store data in both forward and backward directions, while traditional Long Short-Term Memory (LSTM) networks handle data in a specific order. This study proposes a computer vision system that integrates BiLSTM with CNN features for image categorization tasks. The system effectively reduces feature dimensionality using learned features, addressing the high dimensionality problem in leaf image data and enabling early, accurate disease identification. Utilizing CNNs for feature extraction and BiLSTM networks for temporal dependency capture, the method incorporates label information as constraints, leading to more discriminative features for disease classification. Tested on datasets of pepper and maize leaf images, the method achieved a 99.37% classification accuracy, outperforming existing dimensionality reduction techniques. This cost-effective approach can be integrated into precision agriculture systems, facilitating automated disease detection and monitoring, thereby enhancing crop yields and promoting sustainable farming practices. The proposed Efficient Labelled Feature Dimensionality Reduction utilizing CNN-BiLSTM (ELFDR-LDC-CNN-BiLSTM) model is compared to current models to show its effectiveness in reducing extracted features for leaf detection and classification tasks.

Cyber-Physical Systems (CPS) represent an integration of computational and physical elements, revolutionizing industries by enabling real-time monitoring, control, and optimization. A complementary technology, Digital Twin (DT), acts as a virtual replica of physical assets or processes, facilitating better decision making through simulations and predictive analytics. CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains. This survey explores their synergy, highlighting how DT enriches CPS with dynamic modeling, real-time data integration, and advanced simulation capabilities. The layered architecture of DTs within CPS is examined, showcasing the enabling technologies and tools vital for seamless integration. The study addresses key challenges in CPS modeling, such as concurrency and communication, and underscores the importance of DT in overcoming these obstacles. Applications in various sectors are analyzed, including smart manufacturing, healthcare, and urban planning, emphasizing the transformative potential of CPS-DT integration. In addition, the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive, scalable, and secure CPS-DT systems. By synthesizing insights from the current literature and presenting a taxonomy of CPS and DT, this survey serves as a foundational reference for academics and practitioners. The findings stress the need for unified frameworks that align CPS and DT with emerging technologies, fostering innovation and efficiency in the digital transformation era.

Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node’s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental monitoring and Intel data sets demonstrate the algorithm’s effectiveness compared to other benchmarks.

In the relam of the Internet of Things (IoT), prevalence of missing data due to continuous data collection by smart devices necessitates the essential preliminary step of data imputation before engaging in information mining activities. IoT data exhibit robust interconnections in both spatial and temporal dimensions, surpassing the limitations of Euclidean space. Yet, prevailing machine learning and deep learning approaches often focus solely on temporal attributes or capture spatial features exclusively within a Euclidean framework. To address these challenges, this paper introduces a novel network named ST-Bi-LSTM-VAE (Spatio-Temporal Bidirectional Long Short-Term Memory based Variational Auto-Encoder). The architecture of ST-Bi-LSTM-VAE is primarily grounded in the Variational Auto-Encoder (VAE) framework. This innovative approach incorporates two distinct types of VAEs. The first type is dedicated to computing the adjacent matrix of the device network, a crucial input for the Graph Convolutional Network (GCN) essential in capturing intricate spatial relationships among devices. The second type of VAE is specifically tailored for data imputation, leveraging both global spatial and temporal dependencies. Empirical experiments conducted on diverse publicly available datasets substantiate the efficacy of ST-Bi-LSTM-VAE. The results obtained consistently demonstrate that proposed method surpasses baseline techniques in maintaining pattern, structure, and trend across datasets even at 50% missing gap for imputation task with 4.91% performance improvement in case of Intel Berkley Research Laboratory (IBRL) dataset and 3.5% on PRSA dataset.

Inventory management is a part of the supply chain where inventory and quantities of stock are tracked in and out of the stockroom. Proper handling of inventory will leads to successful supply chain management in any organization. QR codes make this inventory management speedy. This fast information transfer will also reduce the number of errors in inventory records and also gives accurate results to make informed decisions during frequent reviews. But based on Quick Response (QR) code for inventory management will become a centralized database. Blockchain facilitates manufacturers to connect each party from distribution centers and retail partners, to suppliers and production sites - with an abiding record of each, single exchange that occurred. These put away records are available to everybody inside the P2P organize and gives decentralization. The degree of straightforwardness and permanency gave in blockchain are frequently useful for manufacturers to oversee item roots and traceability. Smart contracts, one of the features of blockchain, have built-in automation, which makes a lot of sense for transaction management. In this paper, we are using both the features of QR code and blockchain for transparent, distributed, and reliable inventory management.

Blockchain is inventive approach to manage taking care of information, executing trades, performing limits, and working up trust in an open space. Many consider square chain as an advancement accomplishment for cryptography and digital security, with use cases going from comprehensive sent computerized cash structures like Bit-coin, to sharp understandings, insightful cross sections over the Internet of Things, and so forth. Regardless of the way that Blockchain has gotten creating interests in both academic network and industry in the progressing years, the security and insurance of Blockchains continue being at the point of convergence of the conversation while sending Blockchain in different applications. This paper presents a total layout of the security and insurance of Blockchain. To empower the discussion, we at first present the idea of Blockchains and its utility concerning Bit-coin like on the web trades. By then we portray the essential security properties that are maintained as the fundamental necessities what's more, building discourages for Bit-coin like advanced cash structures, trailed by presenting the additional security what's more, insurance properties that are needed in various Blockchain applications. Finally, we review the security and assurance systems for achieving these security properties in Blockchain-based structures, including delegate accord figuring's, has joined limit, mixing shows, puzzling imprints, non-instinctive zero-data check, and so on. We surmise that this investigation can help per clients with increasing an all-around perception of the security and assurance of Blockchain concerning thought, qualities, systems and structures.

As per inclusive status report on road safety-WHO, India has the highest number of road accident related deaths, standing at 1.51 lakhs in 2018 alone of which national highways account for over 30percent. 5G technology is expected to be auctioned by the end of the year and is expected to be available in next 2 years, the technology which has latency of less than 12 milliseconds, which is 3 times faster than the average driver, the potential of 5G alone in saving the lives in enormous. The paper aims to come up with an integrated solution to reduce the motor vehicles collision specifically tailored to Indian road scenarios. The feasibility to Indian roads, expected policy thrusts, market viability, economic concerns-job loss, viability gap funding, infrastructure needs; with special focus on the available and possible technological integration with the help of Internet of things and Internet of vehicles is discussed in depth.

The utilization of various assessment measures for grouping different tasks have picked up a lot of consideration in previous decades extraordinarily for such issues through different and redundant classes. A classifier is proposed particularly to advance one of the conceivable measures, to be specific, the hypothetical G-mean method. In any case, the method is general, and it very well may be utilized to streamline bland assessment measures. The streamlining calculation to prepare the classifier is depicted, and the numerical plan is tried demonstrating its ease of use and power. The proposed oversampling calculation alongside a cost- reduction SVM classification is appeared to enhance execution when contrasted with other estimated strategies on numerous benchmark imbalanced informational indexes. What's more, a various leveled system is produced for multiclass imbalanced issues that have a dynamic class arrange. A novel structure for kernel space instruction in a limited space named Sensible Kernel Space (SKS) is introduced in this manuscript. The SKS can be expressly worked by utilizing any optimistic clear bit counting Gaussian BCG bit by means of an exact portion outlined. The proposed sensible Kernel space can ideally choose various subsets of recently mapped datasets in SKS keeping in mind the end goal to enhance the speculation execution of the classifier.

Over the span of extension in mobiles, amount of nodes in the Mobile Ad-hoc Networks (MANETs) is increasing frequently. Because of dynamic nature of MANET, it frequently undergoes routing issues resulting in packet droppings. As MANETs are mainly used for secured communication which can be established during natural calamities, battle fields, etc., the proposed work focuses on secured route identification process for information exchange with less proportion of packet loss. Secured data communication is possible only if the selected route is more secured and avoids malicious activities. The proposed work considers only registered and trusted nodes for communication. In this paper the proposed method uses 2-ACK method using secured route for overcoming the issue of attacks during routing for requested node. In this paper routing process is done using AODV which reduces the packet droppings in the network. The performance of the proposed method is better than the existing methods.

This article overviews a chain based methodologies for a few security administrations. The Existing framework is contrasted and the proposed framework and it was discovered that the proposed framework has preferred execution over the existing one. Square chain offers a creative way to deal with putting away data, executing exchanges, performing capacities, and building up trust in an open situation. Many consider the square chain as an innovative leap forward for cryptography and digital security. Square chain administrations incorporate verification, secrecy, security and access control list (ACL), information and asset provenance, and uprightness affirmation. Every one of these administrations is basic for the current appropriated applications, particularly because of the huge measure of information being handled over the systems and the utilization of distributed computing. Square chain method is furnished with validation, inspecting, and responsibility, and consequently, it can fill in as a promising instrument for giving secure information correspondence on the system. Validation guarantees that the client is who he/she professes to be. Privacy ensures that information can't be perused by unapproved clients. Protection gives the clients the capacity to control who can get to their information. Provenance permits an effective following of the information and assets alongside their proprietorship and usage over the system. Trustworthiness helps in checking that the information has not been changed or adjusted. These administrations are right now overseen by concentrated controllers, for instance, a declaration authority. Along these lines, the administrations are inclined to assaults on the incorporated controller. Then again, the square chain is made sure about and disseminated records that can help settle a large number of the issues with centralization. From a security viewpoint, the square chain is made and kept up utilizing a distributed overlay arrangement and made sure about through shrewd and decentralized use of cryptography with swarm processing. Block chain offers an imaginative way to deal with putting away data, executing exchanges, performing capacities, and setting up a trust in an open environment. Many consider the square chain as an innovative leap forward for cryptography and digital security, with use cases running from internationally conveyed digital money frameworks. A decentralized distributed storage arranges has been presented with numerous favorable circumstances over the server farm based capacity. Comparable to conventional arrangement, decentralized distributed storage organize use customer side encryption to keep up information security.