The management of crops from the early to mature stage contains nutrient deficiency, monitoring plant disease, controlling irrigation, and controlling the use of pesticides and fertilizers. Moreover, lack of immunity and climate changes cause the crops and minimize the growth of agriculture due to crop disease. The identification and detection of crop diseases is the most challenging task due to less detection accuracy, overfitting, and error rate. So this research work designed a novel Krill Herd based Random Forest (KHbRF) for the accurate detection of crop disease, enhancing the performance of detection accuracy by using an optimized fitness function. The krill herd fitness function is updated to the classification layer for effective crop disease detection. Furthermore, development involves preprocessing, segmentation, feature extraction, and classification. The developed framework is implemented in the python tool, and the plant villa image dataset is tested and trained in the system. After that preprocessing removes errors and feature extraction extracts the texture features from the crop. At last, the classification layer detects the crop disease present in the dataset using the fitness of the krill herd. Additionally, attained results of the developed framework are compared with other state-of-the-art techniques in terms of detection accuracy, sensitivity, F-measure, and error.

The burial of bodies became a trend in the cause of ongoing pending (Novel Coronavirus), more than50 a million people all over the globe are adversely affected, hence the analysis and forecasting techniques are necessary to regain the human livelihood. The enlargement of technologies such as Artificial Intelligence, Machine Learning, Deep Learning, are en route into all the living aspects. Hence by using AI, ML, DL, Advanced technologies and existing models ARIMA, PROPHET, SVM, RNN, Faster & Mask R-CNN, RESNET-50, and other techniques such as logarithmic scaling and exponential smoothing so on, the spread of VIRUS, the effect of countries economic growth, confirmed cases, fatality rate, recoveries are predicted to overcome the life threat due to SARS. Such that different predictive techniques are used to forecast. The advancement in the past algorithms to acquire accurate results are been introduced and described.

In a populous nation, crime is inescapable. All claims are taken to court, ranging from trivial misdemeanors to notable felonies. Factoring out criminal law, there is civil and common law. Most cases of which are also presented in court. 3.5 crore cases are pending, of which 2.4 crore cases are solely criminal. This thoroughly exhausts our judicial system. Artificial Intelligence (AI) has long been used for data analysis and interpretation. The domain of AI has been utilized in numerous judiciaries and is now slowly creeping its way into Indian Law. The initiation of AI into legal studies has the potential to remodel and assist the judicial system. In addition to assisting the courts, AI can also aid law enforcement in solving cases and maintaining the community’s prosperity.

Among the optical neural networks, the dynamicity, complexity, bear dramatically improved payable according to the wideness over superior luminous techniques, and the optical cross-connect technologies then diverse community infrastructure possess great challenges into the optical network management then preservation because of the community operators. The article established a time-domain-based artificial intelligence radar provision because of gesture consciousness utilizing a 33 GS/s direct copy approach. For each static yet strong gesture, a 1-D convolutional neural community is used, with consciousness costs of 93.2 percent and 90.5 percent achieved. The radar rule is shaped including an a65-nm CMOS method so much consumes 95mW concerning power.

The static random access memory (SRAM) Array is used in applications such as cache memories, microprocessors, and portable devices such as smart watches and mobile phones. As technology advances to the submicron level, power dissipation becomes a major disadvantage in SRAM cells, necessitating the development of low-power applications. As a result, it's important to design a memory that consumes less power. The main motive of this paper is to design 4*4, 8*8, and 16*16 SRAM array using 6T and 7T SRAM cells using Graphene Nano Ribbon Field Effect Transistor (GNRFET) technology and compare power dissipation between Complementary Metal Oxide Semiconductor (CMOS) and GNRFET technologies. The MOS-GNRFET HSPICE libraries in HSPICE TOOL is used to perform transient analysis for the Read and Write operations.

Current biomedical applications are using numerous wired and wireless sensors that keep track of various biological signals. These signals are continuously tracked, processed, and monitored for different biomedical applications. In this process, power consumption of the sensor is one of the important parameters to investigate. Most of the signal processing mechanisms is performed in mobile or computer-based applications. But data conversion and transfer can only be performed in sensor hardware where basic arithmetic operations are evaluated on voltages or current values. This work make use of a new and simple method to perform analog arithmetic operations, in which signals are interpreted and also stored using the memristor, rather than voltage or current. A new circuit is also being developed for programming the memristor's memristance with a predetermined analog value. The power dissipated by CMOS EX-NOR gate is highest, the power dissipated by memristor NOR gate is least. This paper will use the LTspice simulator, a common version of SPICE, to address memristor behavior.

The Global Positioning System (GPS) is a satellite-based radionavigation system that uses stored map information to send location and time data to a GPS receiver. One of the disadvantages of GPS is that it lacks memory, meaning it does not recall or learn from past understandings. A simple way for altering a GPS navigational system to include a learning model based on speeds. It is assumed to be totally self-contained, that means no user input or involvement is required. All the necessary data is obtained from the GPS time, position, and date. Another way can be adding the labels or tags. These velocity profiles are used to extract characteristics from the surroundings in addition to learning, which may then be utilized to increase the accuracy of optimal route selection. The RoadTagger is a program that employs Neural Network to estimate the nature of obstacles on road.

Attention as a key aspect of brain activity is one of the most usable area of brain study. It has a significant impact on the brain activities such as learning process and critical activities like driving vehicles. As real-time bidirectional linkages between living brains and actuators, brain-computer interfaces (BCIs) have showed considerable promise. The area of BCIs has been accelerated by artificial intelligence (AI), which can improve the analysis and decoding of brain activity. This paper deals with how attention of a person is detected using Electroencephalogram (EEG) and Brain Computer Interface (BCI).

The catastrophic outbreak of the Novel Corona virus (Covid-19) has brought to light, the significance of reliable predictive mathematical models. The results from such models greatly affect the use of non-pharmaceutical intervention measures, management of medical resources and understanding risk factors. This paper compares popular mathematical models based on their predictive capabilities, practical validity, presumptions and drawbacks. The paper focuses on popular techniques in use for the predictive modeling of the Covid-19 epidemic. The paper covers the Gaussian Model, SIRD, SEIRD and the latest θ-SEIHRD techniques used for predictive modeling of epidemics.

A Brain Computer interface (BCI) is a direct communication pathway between the external devices and human brain, without any dependency on muscles and peripheral nerves. Over the past few years, the Research on Brain Computer interface (BCI) is explored due to recent advance technology in the small and compact electronic methods and electrodes for the people who are ‘locked-in’ or ‘paralyzed’. A detail review on BCI, phases of BCI, human brain and different mechanism to extract the signal is studied. The BCI is a great invention in the area of brain mapping sciences; it is a study of brain functions and spinal cord which helps to identify the damaged functionalities of the body in which thoughts of as practically. The main aim of BCI research is to help the people affected by paralysis and some disorders like brain stem stroke, spinal cord injury and cerebral palsy. Current research focused on non-invasive categories of the Brain Computer interface. This technology depends on the development of various application areas such as entertainment, experimentation, medical, gaming, learning the thought into actions.

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

Brain Computer Interface (BCI) is a technology in which we control the machines with the help of the brain waves, thus the brain waves play a major role. The attention and meditation levels are very helpful in many aspects to know the strength of the attention of the children to the lecture and also used for the medication for the people with ADHD. Many applications (apps) are developed which give the visual representation of the brain waves like graphical representation, charts and also gives the values of different frequencies of the brain waves which are very helpful in the field of technology, medication and for designing games. There are many apps which are not supported by all the devices and many apps are not free of cost. In this paper, we are going to suggest and compare some application software which is freely available on the android Google store which displays the attention level or meditation levels or both and the features of the apps and calculate the accuracy or performance of the apps.

Specialists are dealing with the improvement of EEG-based human–computer interface for upgrading the personal satisfaction in restorative and additionally non-medicinal applications using the blink of eyes. Such innovation can be consolidated to brain science, anesthesiology, gaming, security framework, and for continuous patients checking. It is easy to use the Neurosky Mindwave headset gadgets, which are for the most part used to identify and measure electrical action of the client’s temple and transmit the gathered information remotely, to a computer. Subsequent to preparing EEG signal, it is classify into different recurrence groups for highlight extraction. This paper for the most part deals with extricating the component of EEG sign in OpenViBE. Here, the characteristics and specification of EEG-based HCIs for real-time applications are presented. Furthermore, the discussion about the mental or behavioral state of the person (eyeblink, meditation, attention levels) through the NeuroSky Mindwave (MW001) device using OpenViBE has been done.

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG) based human computer interface in order to enhance the quality of life for medically as well as non-medical applications. Industry and community of research have been attracted by wireless EEG reading devices and they are easily available in the market. Such technology can be incorporated into psychology, anesthesiology, and for real-time patients monitoring. A brain computer interface (BCI) is a direct communication channel between the human brain and the digital computer. In this paper, we present a review on characteristics and specification of EEG-based human computer interfaces for real-time applications using wearable or wireless EEG devices.

The speedy raising insalubrious environment, where peoples have less focus towards own health and physical fitness has impel the need of caregivers and doctors. In addition, increase in elderly population and numerous chronic diseases fortify the need of real-time healthcare system to monitor numerous physiological sign at their doorstep. The advances in sensing, computing and communication technologies coupled with level of comfort, cost reduction, and continuously health monitoring have led to the development of wireless body area networks (WBANs). Development of WBANs impels to make use of wearable and implanted sensor devices for various physiological sign monitoring. Furthermore, it provides long term, real-time healthcare monitoring to individual personal without restricting their daily life activities. In this paper, we proposed a general real-time healthcare monitoring system architecture. We also summarised the need, challenges and radio technologies of WBANs, since it is the key factor to achieve low power and low latency.

This research paper presents path following two wheeled compact portable robot with arduino nano as cental driving functional unit with novel features of wireless control using wifi and bluetooth module with collision detection, avoidance and control features which provides the unique ability of danger avoidance, falling from a hieght with improved stablity and precision control. The extremely sophistcated design provides very good controlled movement on horizantal ground terrain surfaces with data collecting and processing capabilties. The design is integrated with infrared sensors, bluetooth module, wifi module control with dc gear motors which controls the speed of the vehicle of the robotic vehicle and avoid collision with any obstacle detected in the path of the robot. It has the unique abilty of running in maze with path following abilties controlled from any remote location using WiFi for long range control and bluetooth for short range control. In this research article a entire system is designed and implemented in which movement is stably controlled based on feedback from infrared transreciever module. A low cost robust portable design using GUI control has been implemented with advanced features which makes it very unique and attractive for commercial production.

Picture polynomial math depends on regarding a picture as raised as convex polygons (2d) or convex polyhedrons (3d). A generalized formula for the generation of total number of convex polygons and polyhedrons and total no of G-filters for a given structuring elements is explained in this paper.

In recent years, a vast researches are concentrated towards the development of EEG based human computer interface in arrangement to enhancing the quality of life for medical as well as non-medical applications. Industry and community of research has been attracted by wireless EEG devices and they are easily available in the market. Such technology can be incorporated to psychology, anesthesiology, and for real-time patients monitoring. The Neurosky Mind wave headset device was generally utilize to detect and measure electrical activity of the user's forehead and transmits the collected data wirelessly, to a computer for further processing. After processing data base, the signals are categories into various frequency bands for feature extraction. In this paper, we present the characteristics and specification of EEG based human computer interfaces for real-time applications. Furthermore, we discuss about the mental or behavior state of the person (Eye Blink, Meditation, Attention levels) through the NeuroSky Mind wave (MW001) device using Openvibe.

Ultra-wideband (UWB) technology, standardized by IEEE 802.15.6 TG-6 for ultra-low power consumption, is one of the fastest growing technologies for short range communications. In this paper, we propose a novel swastika slot UWB antenna for body-worn communication in Wireless Body Area Networks (WBAN). This paper investigates the effect of antenna-body gap in terms of reflection coefficient, real and imaginary part of input impedance, group delay, Voltage Standing Wave Ratio (VSWR), and radiation patterns in free space and modeled layered human body tissue phantom. The antenna designed in such a way that it can give good performance over free space as well as over modeled human tissue phantom without any prerequisite. The entire observation will carried out at three different frequencies: 5.5 GHz, 7.5 GHz and 9.5 GHz.