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Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves.

n order to provide protection against DoS assaults, the security system interfaces with the AODV routing protocol using techniques based on Route Request (RREQ). It employs not one but two different kinds of techniques. When an inside hostile node launches a DoS attack by flooding the network with RREQs, this strategy may be used to minimise the impacts of the assault in pure AODV routing protocol. In this scenario, the DoS attack is carried out by flooding the network with RREQs. The node in the centre of the network both observes and participates in the conversation that is taking place between the evil node, the sender node, and the recipient node. One of the intermediate nodes on the ideal route, which has a total of four nodes, is comprised of the malicious node and three of its neighbours. This makes up one of the nodes on the ideal path. The malicious node inundates the network with traffic by continuously submitting 10 RREQ queries at each and every second. Due to the fact that its close neighbours are aware of the RREQ_RATELIMIT, each of them will only transmit a maximum of 10 RREQs to it at any one time. Due to the fact that these nodes are only able to receive a maximum of three RREQs from their neighbours in a single second, the neighbours of these nodes are required to transmit a total of seven RREQs. In addition, the RREQs of a total of four must be sent by the neighbours of the nodes in question. Because the resources of the malicious node's neighbours are totally occupied in processing and forwarding the RREQ's that originate from it, the path that connects the blue nodes, if it is formed at all, will consist of a greater number of intermediate nodes. This is because the malicious node is the source of the RREQ's that are being processed and forwarded by its neighbours. This is due to the fact that the RREQs that come from the malicious node have an effect on the nodes that are neighbouring it. As a direct result of this, a denial of service attack is carried out since the legitimate nodes are disconnected from the services offered by other nodes, whose resources are being wasted as a direct result of the flooding. During the phase of the AODV scheme's operation in which two legitimate nodes are attempting to communicate with one another, a malicious node will bombard the network with RREQs in order to disrupt the process. According to this strategy, there is a maximum number of RREQs that may be obtained from a single neighbour, and that number is capped at that amount.

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also these cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framework in order to provide strong data access control among the multiple users. In this paper, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases, data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering and security framework has better computational efficiency than the conventional approaches on large medical decision patterns.

The most challenging problem before the world is providing enough food for the gigantic leap in population. There are numerous reasons for the shortage of food and technological innovations in agriculture are required to overcome the shortages in food supply. Sustainable Development goals (SDGs) provides the futuristic vision and ICT along with other latest technologies will help achieve these development goals at a brisk speed. Recent technological developments as use of mobile-broadband access devices, Internet of Things (IoT), Specialized Robots, Drones, big data analytics and Artificial Intelligence has given farmers tools and technologies to scale agricultural production and marketing of their agricultural products. In this paper, we will discuss how Smart Agriculture is changing the face of agriculture in Budaun, a small city in Uttar Pradesh. Smart agriculture coupled with block chain technology is being used to achieve sustainability in agriculture growth. The production/yield has risen by over 20% and the profits have increased by over 30% with the new technology.

The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions’ features.

Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

Background. Even in today’s environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one’s well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results. Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

With the current data-driven era, there is the potential to employ controllers that can store a large amount of data, which is not achievable with traditional controllers. Our goal is to propose an intelligent controller system for computer room management based on artificial intelligence that maintains data integrity, saves memory, minimizes computation, and simplifies program design. To upgrade the computer room management system’s intelligence that is not high, the management mode that is not flexible, and the distributed large-scale management of the whole school that is difficult to realize, the original system is improved to the distributed computer room management system based on artificial intelligence. By starting from the actual situation of higher vocational college computer room, combined with the characteristics of the school computer room, we designed framework model based on distributed artificial intelligence machine room management system, the system by means of network communication technology and database access technology, put forward the B/S combined with C/S structure to realize the computer room management system model, and used radio frequency identification technology to develop radio frequency card. The results show that the optimization results of the traditional computer automatic control system in the computer room vary greatly and fluctuate between 0.6 and 0.8, while the control results of the automatic control system in this paper keep stable at 0.8, which can reach the ideal state in a short time. Through the outcome, it can be said that the proposed control method can be used of higher level of automation, flexibility, and robustness which will work effectively. Therefore, the improved system integrates software, hardware, communication, and distributed system technology into one, which greatly improves the control effect of computer automatic control process, and control result of computer automatic control process is more stable and has a certain practical application value.

In the contemporary era of unprecedented innovations such as the Internet of Things (IoT), modern applications cannot be imagined without the presence of a wireless sensor network (WSN). Nodes in WSN use neighbor discovery (ND) protocols to have necessary communication among the nodes. The neighbor discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximum percentage of neighbors discovered. The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots. This work develops a lightweight intrusion detection system (IDS) based on two machine learning approaches, namely, feature selection and feature classification, in order to improve the security of the Internet of Things (IoT) while transferring medical data through a cloud platform. In order to take advantage of the comparatively cheap processing cost of the filter-based technique, the feature selection was carried out. The two methods are found to have certain drawbacks. The first category disturbs the original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbors. When the second category is followed, it may have inefficient slots in the wake-up schedules leading to performance degradation. Therefore, the motivation behind the work in this paper is that by combining the two categories, it is possible to reap the benefits of both and get rid of the limitations of both. Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring the original integrity of wake-up schedules and adding efficient active slots. Thus, a Hybrid Approach to Neighbor Discovery (HAND) protocol is realized in WSN. The simulation study revealed that HAND outperforms the existing indirect ND models.

Every human being has emotion for every item related to them. For every customer, their emotion can help the customer representative to understand their requirement. So, speech emotion recognition plays an important role in the interaction between humans. Now, the intelligent system can help to improve the performance for which we design the convolution neural network (CNN) based network that can classify emotions in different categories like positive, negative, or more specific. In this paper, we use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio records. The Log Mel Spectrogram and Mel-Frequency Cepstral Coefficients (MFCCs) were used to feature the raw audio file. These properties were used in the classification of emotions using techniques, such as Long Short-Term Memory (LSTM), CNNs, Hidden Markov models (HMMs), and Deep Neural Networks (DNNs). For this paper, we have divided the emotions into three sections for males and females. In the first section, we divide the emotion into two classes as positive. In the second section, we divide the emotion into three classes such as positive, negative, and neutral. In the third section, we divide the emotions into 8 different classes such as happy, sad, angry, fearful, surprise, disgust expressions, calm, and fearful emotions. For these three sections, we proposed the model which contains the eight consecutive layers of the 2D convolution neural method. The purposed model gives the better-performed categories to other previously given models. Now, we can identify the emotion of the consumer in better ways.

India is having great period of prosperity and importance of sustainable development is increasing day by day. Two thirds of countries economic output is generated from cities, as a result of which large number of Indians have started migrating to urban areas to find employment. By 2030, it is predicted that about sixty eight cities of India will have more than one million inhabitants and six megacities with more than ten million each .Rapid growth in cities give rise to many challenges like limited access to sufficient health care and not enough power supply. City development and planning is crucial for sustainable development in order to meet the challenges of continued growth without destroying the environment and social harmony.

We are living in the era of Smart cities where everything is planned and systematic. The problem we are facing is of the population, which is rising rapidly. In recent years, urban migration has skyrocketed. This has resulted in the rise of garbage waste everywhere. Dumping of garbage in public places creates a polluted environment in the neighborhood. It could cause a number of serious diseases to the people living around. This will embarrass the evaluation of the affected area. In order to reduce waste and maintain good hygiene, we need a systematic approach to tackle the problem. We propose a solution to this waste problem which manages the garbage waste smartly. This research paper proposes an IoT based smart system based on clean waste management that assesses the level of waste on dustbins through sensory systems. In this system the microcontroller is used as a visual connector connecting sensor and the IoT system. We have worked for a village in native village of UP under the scheme Unnat Bharat and the results have been promising.

In 2009, the evolution of the blockchain technology was the foundation of the first ever cryptocurrency, bitcoin. It not just changed the financial industry but also proved to be a major break-through for peer-to-peer data transfer, information exchange having high security, efficiency, and transparency. Blockchain is a database of records which is distributed on the network, or we can say, its a public ledger of all transactions that have executed and are shared between every user on that blockchain network. Some of its characteristics include anonymity, irreversibility, immutability, decentralization and persistence. It has found applications and use in almost all fields which require data sharing but with secure authentication, anonymity and permanence. Some of the areas of its application are finance, real-estate and IoT. In this paper we discuss the blockchain technology and its fundamentals, its working, types of blockchain, applications, advantages, disadvantages and some other aspects in details. In this research paper, we introduce a model of e-voting as a CASE study which is developed in decentralised environment on the Ethereum blockchain. Using the concept of Blockchain, e-voting will assist to strengthen security and integrity of the election system reducing the cost while increasing the privacy. The use of encryption helps to provide security to the system. No intruder can gain access to the voting system and tamper it and in this way, EVMs can’t be accused of being fixed.