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In the current training, Artificial Neural Network (ANN) is utilized, which tuned by Particle Swarm Optimization (PSO) to the challenge of predicting for finance applications. Several researches have shown that ANN-based strategies are trustworthy ways to estimate LSM. Most ANN training methods, however, struggle with serious issues including poor learning rates and getting stuck in indigenous smallest amount. Optimization algorithms (OA) like PSO container increase ANN presentation. PSO prototypical applications to ANN exercise not engaged in success planning to determine network design or relevant elements. Thus, the current work concentrated scheduled the request of a mixture ANN prototypical to the forecast based on fuzzy. For the ANN and PSO-ANN network models, a huge amount of statistics (a record with 168970 preparation records and 42243 challenging records) was collected after the Finance application. This data was used to make exercise and challenging datasets. All of the PSO algorithm variables (including the system limitation and system loads) remained tuned to provide maximized ROI. The projected outcomes (e.g., from ANN, PSO-ANN) aimed at together records (e.g., training and testing) of the models were calculated by one numerical catalogs, namely, Root-Mean-Squared Error (RMSE). As a consequence, together replicas displayed worthy presentation; nevertheless, the hybrid ANN model might outperform ANN in terms of performance, as determined by the ranking mechanism that was created. For the ANN and hybrid ANN replicas, it container be derived that the PSO-ANN prototypical demonstrated more dependability in predicting compared to the ANN.

Wireless sensor network comprises of a large number of sensor nodes to acquire and transmit data to the central location. However, due to resource constrained nodes, deployment strategies and communication channel introduce numerous security challenges to the wireless sensor networks. So, it is essential to detect unauthorized access to improve the security features of wireless sensor networks. Network intrusion detection systems provide such services to the network and it becomes inevitable for any communication network. Machine learning (ML) techniques are widely used in intrusion detection systems; however, the performance of ML techniques is not satisfactory while handling imbalanced attacks. To solve this and to improve the performance, this research work proposed an intrusion detection system based on deep neural network (DNN). Cross-correlation process is used to select the optimal features from the dataset and the selected parameters are used as building blocks for deep neural network structure to find intrusions. The experimental results confirmed that the proposed DNN performs better than conventional machine learning models such as support vector machine, decision tree, and random forest and efficiently identifies the attacks.

Exact determination of cotton fibre quality is a huge help for turning factories to purchase cotton as per the least prerequisites. The nature of cotton fibre is decided on numerous elements, for example, staple length, development, finesses, neatness, tenacity, and quality, to make reference to. The essential target of the framework is to accomplish the institutionalization at various levels in the cotton production network the board in the conveyed mechanical systems condition with exact estimation and characterization procedures. The current framework is executed in information mining procedures to foresee the nature of cotton fibre and to establish the most noteworthy connection between's short fibre file and yellowness. The proposed framework incorporates a bunching strategy to group cotton quality fibre. The proposed optimized principal component analysis (OPCA), and Fuzzy C-means bunching in huge dataset. These proposed calculations are executed inside the Map-Reduce method and cuckoo searching technique is applied for reduces the time complexity it is implemented in Hadoop stage to anticipate dampness content, micronaire, extension, and group quality.

The applications of Wireless Sensor Networks (WSNs) are been broadly utilized in the field of Internet of Things (IoT) under communication framework. Notwithstanding services gave by the WSNs, numerous IoT-related applications necessitate reliable and secure delivery of data over unsteady remote connections. In-order to ensure secure and reliable delivery of data, many existing paper works accomplish authentication based routing algorithms with numerous forwarders within the Wireless Sensor Networks. Be that as it may; these types of approaches are vulnerable to genuine attacks like Denial of Service (DoS), where countless duplicate data packets are intentionally dispatched to destination node which disturbs the typical activities of wireless sensor networks. So, here we propose a new scheme of security algorithm for the wireless sensor networks. Our method, Biometric based-Authenticated Geographic Opportunistic Routing (BAGOR) algorithm depends on the user biometrics to shield the violation of DoS attacks, in order to meet out the validness requirements and reliability in the network. By examining biometric and statistic state information (SSI) of remote connections, BAGOR uses a trust model as statistic state information to get better proficiency of packet delivery. Dissimilar to past pioneering routing algorithm, BAGOR guarantees data honesty by building up an entropy-deployed selective validation algorithm and can detach DoS aggressors and reduce the computational expense. Thus, the eveloped procedure is assessed and compared with already existing security techniques. The simulations show that BAGOR decreasing system traffic, shielding against Denial of Service attacks, and expanding the lifetime of a sensor node in the network. Thus, the usefulness and execution of the whole system is enhanced.

The most significant motivation behind calculations in data mining will play out excavation on incomprehensible past examples since the extremely large data size. During late occasions there are numerous phenomenal improvements in data assembling because of the advancement in the field of data innovation. Lately, Privacy issues in data Preservation didn't get a lot of consideration in the process mining network; nonetheless, a few protection safeguarding procedures in data change strategies have been proposed in the data mining network. There are more normal distinction between data mining and cycle mining exist yet there are key contrasts that make protection safeguarding data mining methods inadmissible to mysterious cycle data. Results dependent on the data mining calculation can be utilized in different regions, for example, Showcasing, climate estimating and Picture Examination. It is likewise uncovered that some delicate data has a result of the mining calculation. Here we can safeguard the Privacy by utilizing PPT (Privacy Preservation Techniques) strategies. Important Concept in data mining is privacy preservation Techniques (PPT) because data exchanged between different persons needs security, so that other persons didn’t know what actual data transferred between the actual persons. Preservation in data mining deals that not showing the output information / data in the data mining by using various methods while the output data is precious. There are two techniques used for privacy preservation techniques. One is to alter the input information / data and another one is to alter the output information / data. The method is proposed for protection safeguarding in data base environmental factors is data change. This capacity has fuzzy three-sided participation with this strategy for data change to change the first data collection.

Opinion mining concept is deployed to predict the trainer evaluation with student's feedback. To examine this feedback concept, where opinion examination helps to distinguish how students are communicated in writings and whether the articulations demonstrate positive (ideal) or negative (troublesome) and conclusions toward the subject. In this research work efficient fusion based neural network (EF-NN) classifier is introduced to predict the frequent context patterns used in the student feedback dataset. Our proposed EF-NN is a hybrid model of both support vector machine and convolutional neural network. Student feedback data set is extracted based on attribute features like the interaction between the students, examination, and notes given, etc., Experimental results can be evaluated on weka toolbox based on this result negative and positive details are collected to improve the efficiency of teaching by faculty to provide the enhanced training. Finally, the result of the accuracy, recall, and precision is compared with the existing K-means clustering method.

As indicated by the Health foundations, illnesses brought about by an undesirable way of life speak to the main source of death everywhere throughout the world. Along these lines, it is pivotal to monitor and evade clients' unfortunate practices. Existing wellbeing observing methodologies despite everything face numerous challenges of restricted knowledge because of deficient medicinal services information. Hence, this article proposes a shrewd individual wellbeing counsel for comprehensive and clever wellbeing checking and direction. The monitoring of personal healthcare screens both physiological and mental conditions of the client. The Score obtained from this layout is expected to assess the general wellbeing status of the client. At last, a testbed for check of possibility and relevance of the proposed framework was created. The experimental and reproduction results have indicated that the ace presented approach is effective for appropriate client state checking.

Cloud provides several benefits in e-health care. Based on current trends, the E-healthcare database could be linked to cloud services. This work provides an idea of combining a structure for monitoring electronic-health services based on the cloud environment. With this, we are planning to adapt it to distributed computing. This framework has been enhanced in order to provide a variety of health services. The whole framework of the E-Healthcare Database Maintenance System covers the consumer side and the application side and Cloud. In order to improve safety in the e-health system, the biometric confirmation system is added in order to avoid non-criminal situations and to forget about the password situation. In addition to this framework, we add a security module to enhance the safety and privacy of patients. Our proposed framework improves the security, privacy, time, and cost of access to health information.

The work that has been taken enforces the artificial intelligent technique in providing framework to design and make calculations based upon the discovers made by following the examples in learning technology to make a relationship successful for the appraisal of the work done with the enormous amount of information shared using collective learning ambience. The Collective learning ambience will be help for the larger team members for a problem in real time world and bring out a solution for the same. Visualizing these organized work flow in a multilayered frame work gives more difficulties in finding out a perfect solution. So to make the process much easier we are going to use a technique that deals around the machine learning framework in obtaining the required data and fulfill the information gathering as an easy process which is to be taken place in the collective learning ambience(CLA). After which the data and information that has been obtained enhanced with the integration technique in finding out the psychometric analysis and deep learning techniques to figure out feature extraction, skill recognition, pattern finding and also finding out the behaviors in human begin based upon the input that has been obtained from various resources. Thereafter the process will also be helpful in finding out the lower level process involved in the learning process.

Intensive cropping and exhaustive nature sugarcane crop in tropical India have led to depletion of inherent soil fertility resulting in serious threat to sustainable sugarcane production. Improving soil organic matter and soil fertility are important factors for sustainable sugarcane production. Microbial consortia comprising of Trichoderma viride, Humicola spp, Paecilomyces lilacinus, Gluconacetobater diazotropicus, Azospiriillum brasilense, and Bacillus subtilis have great potential to recycle crop residue and restore soil fertility which eventually promote sugarcane growth. Field experiments with in situ trash management in plant crop and bio-intensive modulation of sugarcane ratoon rhizosphere in ratoon crops were conducted during 2014, 2015 and 2016 at ICAR—Sugarcane Breeding Institute, Coimbatore, Tamil Nadu, India. The results revealed that green manuring with sunnhemp, in situ trash management and application of 280:62.5:120 kg NPK ha−1 in plant crop sustained the soil health by way of reducing soil bulk density (1.26 kg dm−3), lower soil penetration resistance in the ratoon rhizosphere (1.79, 1.82 and 1.75 MPa) and higher soil organic carbon (0.52%) which in turn increased ratoon cane yield 12.24% (99.02 t ha−1) and sugar yield by 8.64% (12.83 t ha−1) over the control. Further, bio-intensive modulation of ratoon rhizosphere by off barring, trash shredding plus soil incorporation with microbial consortia and application of 350:62.5:120 kg NPK ha−1 in ratoon crop recorded significantly higher cane yield (100.95 t ha−1) and sugar yield (13.19 t ha−1) over the control. The improvement in cane yield and sugar yield to the tune of 16.35 and 14.10% due to bio-intensive modulation of ratoon rhizosphere over control have clearly established that productivity of sugarcane ratoon can be significantly improved with balanced use of fertilizers, green manuring, crop residue recycling and microbial consortia application. Similarly, bio-intensive modulation of sugarcane ratoon rhizosphere (S3) with lowest soil bulk density (1.26 kg dm−3) and soil penetration resistance values (1.69, 1.81, and 1.75 MPa) was found effective in minimising the soil compaction and improving nutrient availability (NPK) with build-up of soil microbial population than control (Trash removal). Based on the results of 3-year field experiments, it is concluded that sunnhemp green manuring and in situ sugarcane trash management coupled with application of 280:62.5:120 kg NPK ha−1 in plant crop followed by bio-intensive modulation of ratoon rhizosphere including off barring, trash shredding and soil incorporation with microbial consortia and application of 350:62.5:120 kg NPK ha−1 in ratoon crop can be recommended for sustaining soil health and sugarcane productivity under wide-row sugarcane planting systems of tropical Indian condition.

A field experiment on water management with composted coir pith and sugarcane trash under two irrigation levels (100 and 75% of recommended level of irrigation) with the aim of assessing performance of bud chip settling in 150 × 45 and 150 × 60 cm spacing was conducted in split-plot design at ICAR-Sugarcane Breeding Institute, Coimbatore, Tamil Nadu, India, during 2013–2014, 2014–2015 and 2015–2016. The soil of the experimental site was sandy clay in texture, taxonomically classified as typic haplustalf, low in organic carbon, medium in available N and available P and fairly high in K, slightly alkaline in reaction with normal electrical conductivity. The prevailing climatic condition during experimentations represented tropical Indian conditions, wherein mean temperature ranged in between 21.24 and 33.10 °C with a mean relative humidity of 54.58–85.5%. As against the normal rainfall of 674.2 mm, only 356, 537 and 679 mm of rainfall was received during 2013–2014, 2014–2015 and 2015–2016 crop seasons indicating the erratic behaviour of rainfall. The results revealed that application of 10 t/ha composted coir pith or 5 t/ha of sugarcane trash in furrow at the time of planting and scheduling irrigation in sugarcane at 75% of recommended level of irrigation saved 387, 344 and 255 mm irrigation water; in addition, it gave higher irrigation water use efficiency (0.82 t/ha/cm), net returns (99,039 Rs/ha) and benefit–cost ratio (1.79) over scheduling irrigation at 100% level. Further it was also observed that setts planting at 90 cm recorded 15.47 and 25% higher cane yield than bud chip settling planting at 150 × 45 and 150 × 60 cm, respectively. Amongst two soil moisture conservation measures, i.e. composted coir pith and trash, application tried in sugarcane conserved soil moisture efficiently and recorded higher soil moisture by 8.10 and 7.62% and cane yield 19.59 and 12.91%, respectively, over the control. Planting bud chip settling at 150 × 45 cm spacing was found superior over bud chip settling planting at 150 × 60 cm spacing. Hence for efficient water management in sugarcane under tropical Indian condition, use of composted coir pith @ 10 or 5 t/ha of chopped trash with 75% of recommended level of irrigation in bud chip settling (150 × 45 cm) planting is recommended.

Routing in wireless sensor network has entirely different requirements compared to the routing requirements of a normal network. The complexity arises from the requirements of security, load balancing, resource constraints and failure handling. This paper presents a fast, secure and dynamic route generation technique for wireless sensor networks using metaheuristics for route generation. A modified form of Particle Swarm Optimization technique is used for route generation. In-order to overcome the problem of local optima, PSO is hybridized by incorporating Simulated Annealing in its local selection process. Hybridization also speeds up the route selection mechanism, thereby reducing the time overhead to a maximum extent.

Security and energy efficiency is of paramount importance in a wireless sensor network. This is due to their vulnerabledeployment conditions and battery based power. This paper presents a secure and distributed algorithm that generatesroutes on-demand in a wireless sensor network. Dynamic route generation is facilitated by PSO, a metaheuristictechnique. Current network traffic in that route and charge contained in the candidate node are used as evaluationparameters along with the node distance, hence a huge reduction in the packet loss was observed. Experiments wereconducted and it was observed that the proposed algorithm exhibits very low selection overhead and also providesdistributed routs, which eventually lead to prolonged network lifetime.

Present study envisages the potential of triple layer bags for reducing pulse beetle infestation under laboratory conditions over six months of storage. It was observed that when chickpea grain was stored in jute bags with bruchid infestation, the germination percentage decreased significantly from 84.67 to 18.67% in six months of storage. But, the grain stored in jute bags without infestation the germination percentage decreased from 92.00 to 73.33% only. On the other hand, grains stored in triple layer bags with bruchid infestation, the germination percentage merely decreased from 86.00 to 78.33% in six months of storage and triple layer bags without infestation the germination percentage only decreased from 91.67 to 90.67%. The per cent grain damage over a period of six months storage also increased significantly from 14.54 to 70.67% and 0.00–26.45% in jute bag with bruchid infestation and jute bag without infestation, respectively. However, per cent grain damage in triple layer bags with bruchid infestation increased only from 15.15 to 25.70% and triple layer bags without bruchid infestation showed negligible increase from 0.00 to 2.64% grain damage in six months of storage. The corresponding per cent weight loss of infested grain was increased significantly from 8.51 to 55.67% in six months of storage in jute bag with bruchid infestation and from 0.00 to 15.08% in jute bag without infestation. Whereas, per cent weight loss of infested grain in triple layer bags with bruchid infestation increased only from 7.83 to 15.70 in six months of storage and triple layer bags without bruchid infestation recorded a mere per cent increase in weight loss from 0.00 to 1.48%. Benefit cost ratio showed that grains stored in triple layer bags for 6 months has highest benefit cost ratio (1.23) compared to jute bag storage (0.39).