Among the most common paroxysmal neurological conditions is epilepsy. When spontaneous combustion occurs seizure is a defining feature. An epileptic seizure is caused by a brain syndrome called epilepsy. The electroencephalogram test is useful for detecting epileptic seizures and diagnosing epilepsy because it contains significant physiological data that can reflect human brain activity. The EEG signal (EEGS) is used for capturing the signals from the brain, which helps in the localization of the epileptogenic region and thereby plays a vital role in successful surgery. The signals, both focal and non-focal are attained in the epileptogenic area and normal region respectively. The localization of epileptic seizures with the help of a focal signal is necessary while detecting seizures. Hence, the present article provides a detailed analysis of EEG readings. The Signals with and without focus are decomposed by elliptical mode decomposition-discrete wavelet transform (EPMD-DWT). A combination of the EPMD-DWT decomposition method by log-energy entropy gives an efficient accuracy in comparison to other entropy in distinguishing the Focal from Non-specific signals. The extracted features are subjected to support vector machine algorithm (SVMA) and K nearest-neighbour (K-NN) classifiers whose performance will be calculated and verified for accuracy, sensitivity, and specificity. In the end, it will be shown that K-NN produces the highest accuracy when compared to SVMA classifier. The EEGS categorized into focused and non-focal signals were carried out through the K-NN method whose performance was calculated and verified in terms of their specificity, sensitivity, and accuracy. It was also inferred that with an increase in data at every point, the performance parameters were enhanced and later got saturated after a certain specific point. Further, the K-NN classifier obtained the highest accuracy of 75%, a sensitivity of 77.78%, and a specificity of 72.73% while the SVMA classifier obtained an accuracy of 58.33%, a sensitivity of 60.87%, and a specificity of 56.76%. Thus it can be stated that the K-NN classifier provided the highest accuracy when related to SVMA classifier.

In the age of abundant digital journalism, the categorization of news articles has become increasingly crucial for efficient information retrieval and analysis. This article examines different machine learning and deep learning methods used in classifying news articles, with a specific focus on overcoming challenges presented by imbalanced datasets. Conventional techniques like Logistic Regression, Random Forest, and Decision Trees provide interpretability but face difficulties in achieving precision, especially in imbalanced scenarios. Advanced models such as Multi-Class CNN-LSTM, MLP, and RNN exhibit superior performance by effectively capturing both local and global features in news articles. Assessments across various categories showcase the flexibility and reliability of these models, with MLP consistently surpassing others. The research underscores the significance of choosing tailored models and highlights notable progress in news categorization accuracy through advanced methodologies. Ongoing exploration in deep learning and ensemble strategies shows potential for further improvements in tackling the evolving challenges of news categorization within the expansive realm of digital journalism and big data.

The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two deep learning models: Deeplabv3 + and VGG with a vision transformer. The Deeplabv3 + model is used to segment the macula region in the retinal images. The segmented macula region is then fed into the VGG for feature extraction with a vision transformer model for detection. This approach leverages the strengths of both models in detecting accurately and efficiently. The Deeplabv3 + model can accurately segment the macula region, which is crucial for accurate detection. The VGG combined with a vision transformer model proves highly efficient in detecting even subtle changes in the macular region, signifying the existence of macular edema. The results of our experiments with the dataset show that the proposed method outperforms current cutting-edge techniques. With an outstanding precision rate of 99.53%, the suggested approach firmly solidifies its superiority. The results highlight the effectiveness of the proposed technique in precisely and effectively detecting pathological fluid accumulation in retina images. This ability can have a substantial influence on the early detection and management of eye disorders.

WSNs are now widely used for information gathering and transmission using WSN. Due to its low cost and simple communication, this type of network is widely used in many applications. Although hierarchical routing protocols may handle a variety of applications, choosing a Cluster Head (CH) and balancing network overload are difficult problems. This recommended strategy provides LEACH Protocol based on Novel Trust Management with Cryptographic RSA algorithm (NTM-LEACH-RSA) to extend the lifetime of network and to consume less energy. Here the proposed methodology includes two aspects for improving Security in WSN. Using the suggested NTM-LEACH technique, cluster formation and cluster head election are carried out in the first phase. Here, the threshold function value, the distance and density between nearby nodes, and the trust value are used to elect the cluster head. Based on the energy domain and the distance domain, the threshold function value is estimated in this case. The RSA cryptography technique is employed in the second phase to protect data transmission and guarantee data integrity. By using simulation tools, the proposed NTM-LEACH-RSA methodology's performance analysis is estimated. In comparison to other algorithms currently in use, it also offers higher performance results.

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time. With the use of mobile devices, communication services generate numerous data for every moment. Given the increasing dense population of data, traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation. A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning (RKLSTM-CTMDSL) model is introduced for traffic prediction with superior accuracy and minimal time consumption. The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction. In this model, the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction. A large volume of spatial-temporal data are considered as an input-to-input layer. Thereafter, input data are transmitted to hidden layer 1, where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results. After obtaining the relevant attributes, the selected attributes are given to the next layer. Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns. Tversky similarity index outcomes are given to the output layer. Similarity value is used as basis to classify data as heavy network or normal traffic. Thus, cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model. Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.

The efficiency and security of WSNs are improved by designing multiple lightweight cryptographic techniques and a 6LoWPAN protocol, depending on resource utilization and a reasonable amount of energy. This is still caused by constraints such as power resource management, authentication, key management, and flexibility protocols. To address these challenges, a novel approach called the modified gray wolf-based chameleon swarm (MGW-CS) algorithm is presented to select the cluster heads from WSN nodes. The MGW-CS algorithm is an amalgamation of both improved gray wolf optimizer and chameleon swarm algorithm. During cluster head selection, the QoS parameters such as network lifetime, energy efficiency, and throughput and packet delivery ratio are improved and optimized. By presenting a lightweight and flexible cryptographic model to manage encryption complexities, the encryption parameters are automatically selected based on the currently available resources of each sensor node for data encryption. Among various WSN nodes, data exchanging and secure communication are established with the development of novel authentication and a lightweight key management model. The proposed model is implemented using the NS-2 software. The proposed methodology offers a packet delivery rate of 98.6% for a total of 500 nodes when compared to the different state-of-art techniques.

Data analysis converts raw data into information useful for decision-making. In recent years, the most promising research area is healthcare data analysis. For efficient analysis of data, the critical tool emerged is machine learning (ML) that uses various statistical techniques and algorithms like supervised, unsupervised, and reinforcement to predict the results of data analysis on healthcare data more precisely. In ML, various algorithms, such as supervised learning, unsupervised learning, and reinforcement learning algorithms, are used for analysis. For analyzing different healthcare data, the chapter describes varied categories of ML techniques and commonly used probability distributions in Data Science like Bernoulli, Uniform, Binomial, and Normal (Gaussian) Distribution. In the healthcare field, cloud technology and Internet of Things (IoT) offer several opportunities to clinical IT. It improves healthcare services by identifying the disease caused by the human body and contributing its non-stop methodical innovation in a massive information domain. To manipulate patient records in cloud-IoT environments is still a big challenge because of extensive data. A new model does not require the intervention of human to analyze large volume of data received from numerous origins and also sensor data is presented. Fuzzy temporal neural classifier is applied in the cloud-IoT environment to optimize the secured storage and easy handling of a vast amount of patient records. Presented work pursuits the healthcare system's performance in reducing the execution time of patient's request, optimizing desired garage of patient's massive facts, and imparting records retrieval process for those applications. The experimental analysis outcome of the presented method performs better than existing benchmark systems considering parameters like disease prediction accuracy, sensitivity, specificity, F-measure, and computational time.

Garcinia indica commonly known as kokum, has lot of medicinal properties and commercial importance, but the crop remains neglected and hence there is a need to concentrate on diversification and popularization of such an underutilized fruits through development of value added products. To take advantage of health promoting properties of Kokum, Kokum fruits used as pulp and powder forms for ten different formulated and standardized value added products like Kokum Jelly (KJ), Kokum Squash (KS), Kokum Carbonated Drink (KCD), Kokum wine (KW), Kokum Diet Powder (KDP), Kokum Chutney Powder (KCP), Kokum Rasam Powder (KRP), Kokum Lassie (KL), Kokum Spray Dried Powder (KSDP) and kokum popsicles (KP). Sensory evaluation was conducted on all the products and the results indicate that, KRP were highly acceptable (8.98 ± 0.32) among all other kokum products followed by KJ, KS, KCD, KW, KDP, KCP, KL, KSDP and KP, with good acceptability scores. Value addition of Kokum fruits will improve the consumption by different communities and also reduce the postharvest losses of the Under-utilised fruit, apart from promoting several health benefits.

Due to the increased growth of elderly people in recent years, healthcare systems face many challenges on the money spent for those people. Both quality and affordability has to be provided by the new technology which is the today’s need. When applying WSN technologies, the advantages such as continuous monitoring with alert mechanisms and relative information are to be satisfied. Among the other challenges, due to the deployed environment, security is a key challenge. As gateway connects to the wireless networks, it is the target area for many adversaries to launch various attacks. Initially, attacker launches node compromise attack which leads to node replication attack. The introduced security methods for intelligent healthcare monitoring system effectively detect replication attack and provide protection to the system. The potential application of proposed methods namely Exponential Moving Average based Replica Detection (EMABRD), Secured Ant Colony Optimization (SACOP) and Fingerprint based Zero Knowledge Authentication (FZKA) is applied to a real time environment. While comparing three algorithms, SACOP has higher detection probability of malicious nodes at the expense of increased storage and communication overheads over EMABRD and FZKA. FZKA performs better compared to EMABRD in terms of detection probability but at the cost of increased overheads. So, among the three algorithms, EMABRD is better in terms of overheads and SACOP is better in terms of detection probability.

Interaction of plants to abiotic stress is complex and involve various physiological and biochemical responses. A common response of plants to fungicidal stress is the accumulation of proteins and amino acids. Amino acids and other soluble nitrogenous compounds play an essential role in plant metabolism. Hence the present work was carried out to study the effect of different concentrations of mancozeb on protein metabolism during the germination of paddy cultivars. The seeds were soaked in different concentrations of mancozeb and control was maintained. Seed treatment with mancozeb suppressed the protein content but significantly increased the protease activity, free amino acids and proline content in relation to progressive concentrations of fungicide. Thus mancozeb act as modulator and endow plants with capacity to adapt to stressful condition by biological and physiological adjustment at cellular level.

In Wireless Sensor Networks (WSNs), effective transmission with acceptable degradation in the power of sensor nodes is a key challenge. In a large network, holdup is bound to occur in communicating superfluous data. The aforementioned issues namely, energy, delay and data redundancy are interdependent on each other and a tradeoff needs to be worked out to improve the overall performance. The extant methods in the literature employ either centralized or distributed approach to select a cluster head (CH). In this paper, sink originated hybrid and dynamic clustering with routing technique is proposed. The proposed routing algorithm works based on node handling capability of each sensor node in the selection of CH and also helps in identifying the forwarder node. In addition, processing load of a sensor node is also considered for selecting the forwarder. Both space and time correlation is used to collect data from the clusters and then aggregated to provide a proficient communication. The introduced method is evaluated with the performance of the previously available techniques like, Data Routing for In-Network Aggregation (DRINA), Efficient Data Collection Aware of Spatio-Temporal Correlation (EAST), Cluster-Based Data Aggregation (CBDA), Energy-Efficient Data Aggregation and Transfer (EEDAT), and Distributed algorithm for Integrated tree Construction and data Aggregation (DICA). Simulation parameters considered for assess ing the performance of the proposed algorithm are aggregation ratio, routing overhead, packet delivery fraction, throughput, packet delay and consumed energy. The experimental analysis of the introduced algorithm generates paramount outcome of finest aggregation quality with diverse key characteristics and circumstances as required by a sensor network.

Crop damage is one of the core and perennial problems in agricultural field. Most of the researchers in both academic institutions/universities, government organizations and farmers were concentrating on finding optimized solutions to overcome crop damage occurring due to natural threats. Among many natural threats, crop damage is mainly induced by birds, particularly peacocks in the southern regions. Crops are also affected due to the seasonal variations and in different stages of crop growth. As the national bird of India is Indian peafowl (Pavo cristatus), it must be protected in spite of it causes various threats to the farmland owners. Peacock damages crop in the cultivated farmlands by migrating from forest into semi-rural and residential areas after monkeys. The peacocks only damage crops in the fields, but the monkeys scare the humans by getting closer. A decade back, people (including farmers) were surprised and enjoyed seeing peacock in their place. Now farmers were frightened because it invades their farmlands by damaging their crops, thereby causing severe economic loss to the farmers. In northern region of Tamil Nadu, especially Erode and Coimbatore districts, nearly 65% of the people are directly or indirectly dependant on agricultural sector for economic survival. The paper focuses on helping the farmers to protect the crop damage from peacock. Mostly, in our surrounding, the crops are frequently damaged by peacocks. The farmers used to keep away the peacock from the farmland by making sounds by themselves. But that is ineffective in repelling peacock mainly in large fields. So, peacock repellent technique is proposed to solve the above problem. The paper unveils the importance of using interdisciplinary approach to develop an eco-friendly technique to reduce crop damage without affecting peacock.

Construction of cloud computing and promotion of applications such as social network service and smart city have driven the need for trust mechanism with the rapid developments of Internet of things (IoT). In the existing methods, storing the trust value incurs high storage overhead leading to energy inefficiency and reliability of identifying trustful or untrustworthy node is very less. In order to avoid these drawbacks, Secured Ant Colony Optimization (SACOP) based on trust sensing model is proposed to detect the node replication attack. Firstly, node’s trust value is estimated using direct and indirect trust evaluation model to identify the malicious node in the clustered network. Secondly, ant colony routing algorithm is introduced to select the secured optimal path using probability to select the next hop node for data forwarding. As the probability is calculated using the residual energy, trust and pheromone values, energy expenditure among all nodes gets balanced. The proposed algorithm performs better in terms of packet loss rate, time delay, throughput and average energy consumption compared to existing scheme DDR.

Coconut flour is an excellent source of unique taste and aroma and rich in vitamins, minerals and dietary fibers, which might have potential application in baking products and human nutrition.The study is aimed to investigate the effect of honey and different levels of coconut flour on muffins.Four types of muffins such as, T 1 -0%, T 2 -5%, T 3 -15% and T 4 25% of coconut flour incorporation were investigated and T 4 is found more acceptable in terms of physicochemical properties of muffins.T 4 muffins secured the highest score in color, texture and overall acceptability.The results showed that the addition of 25% coconut flour to the batter has improved the sensory and physico-chemical characteristics of the samples of the muffins obtained, and consequently increased their nutritional value.

Due to the broadcast nature of wireless communication, wireless sensor networks (WSNs) are susceptible to several attacks. Amongst them, replica attack is one of the predominates as it facilitates the attackers to perform some other attacks. So, it is of immense significance to design a competent security method for WSNs. Introducing a trust method is the primary concern for assisting well-organized use of the available energy in each node in the energy restricted environment. In order to tradeoff between energy usage and attack detection, energy-based prediction approach is deemed to be a suitable one. A statistical method, exponential moving average (EMA) model based replica detection is proposed to detect replica node attack based on energy consumption threshold in WSNs. The difference between actual and predicted energy consumption exceeding the threshold level is considered as malicious. In this paper, future energy drop of a sensor node is forecasted using statistical measure instead of probabilistic method. In EMA model, the transition from higher power consuming state (active state) to lower power consuming states (sleep and sense states) is controlled by a fixed schedule. The accumulated average time of the node was in any state in the past is used to estimate the time duration of a node that spends in that state. Unlike Markov Model, the estimations of energy are made periodically. By this, computational overhead on the microcontroller of the sensor is greatly reduced in EMA approach. The simulation results taken using TRM simulator shows that choosing the threshold value which is neither too large nor too small results in optimum level of detection accuracy and lifetime of the network.

Sponge cake prepared by partial substitution of wheat flour with mango pulp and milk powder at different concentrations (control, 35%, 35%, 50% and 50%) and (5%,10%,5% and 10%)were investigated for the physico-chemical, nutritional and organoleptic characteristics. Results showed sponge cake incorporated with mango pulp (50%) and milk powder (10%) to have high dietary fiber, fat, moisture, ash, protein vit-A, fiber and calorie, hydrolysis and predicted glycemic index compared with the control. Increasing the levels of mango pulp and milk powder in sponge cake had significant impact on the volume, firmness and color. Sensory evaluation showed sponge cake formulated with 50% mango pulp and 10% milk powder to be the most acceptable. Mango pulp and milk powder have high potential as Protein -rich ingredients and can be utilized in the preparation of cake and other bakery products to improve the nutritional qualities.

Jack fruit (Artocarpus heterophyllus) is one of the popular fruits in India. It considered to be the Poor man’s food (Prakash et al., 2009). Jackfruit is an underutilized fruit crop. Jackfruit rinds are normally disposed as wastes by food industries and vendors. Owing to its wide variety of applications, a major amount of peel (which constitutes ~ 59% of the ripe fruit) is discarded as waste. Inbaraj, et al., 2006.The roughly annual jackfruit peel manufacture is estimated to be 2714 11,800 kg per tree. These residues create a potential threat as a waste product. Appropriate ways to convert these wastes into value-added merchandise by means that of by-product recovery will serve the twin purpose of environmental protection and value addition. Pectin is a valuable by-product that may be secured from these fruit wastes. These rind have a great potential as a source of many important nutrients.

Present research was carried out to utilize psyllium husk for preparation of digestive cookies. Refined wheat flour was replaced with psyllium husk in different combinations @ 5, 10 and 15%. Regarding characterization of psyllium husk, mean values obtained for moisture, crude protein, fat, ash and fiber and in husk were 6.43±0.05, 2.08±0.06, 0.09±0.01, 3.85±0.04, 3.83±0.02 and 70.03±0.02%, respectively. Physical characteristics of digestive cookies i.e. diameter and spread ratio were diminished with the addition of husk while thickness was increased. Chemical assay revealed higher crude protein content in control cookies. Whereas, moisture, ash and fiber contents were higher in psyllium husk based cookies. Softer cookies with low gross energy were obtained with the addition of psyllium husk. Conclusively, psyllium husk based cookies showed gradual enhancement in dietary fiber content as the amount of husk was increased in the recipe. The composition of digestive cookies found nutritionally superior as well as recorded highest score in sensory properties and it can be concluded that the substitution of wheat flour with isabgol up to 15 per cent and 5ml of pomegranate juice into the formulation of cookies enhanced the Physico-chemical properties as well as sensory properties. The resultant cookies may have the potential to manage the digestion and bowel function in human subjects.