Tuesday, September 30, 2025

What is meant by soft computing?

Soft computing

 Soft computing is a branch of artificial intelligence that develops intelligent systems capable of solving complex, uncertain, and imprecise problems using methods inspired by nature, such as fuzzy systems, artificial neural networks, genetic algorithms, and swarm intelligence. Unlike "hard computing," which relies on exact logic and precise mathematical models, soft computing embraces uncertainty and approximation, making it suitable for real-world scenarios where data is incomplete or ambiguous. Common applications include medical diagnosis, pattern recognition, industrial process control, and home appliance intelligence.  

Soft computing is an approach where we compute solutions to the existing complex problems, where output results are imprecise or fuzzy in nature, one of the most important features of soft computing is it should be adaptive so that any change in environment does not affect the present process. The following are the characteristics of soft computing.

  • It does not require any mathematical modeling for solving any given problem
  • It gives different solutions when we solve a problem of one input from time to time
  • Uses some biologically inspired methodologies such as genetics, evolution, particles swarming, the human nervous system, etc.
  • Adaptive in nature.
There are three types of soft computing techniques which include the following.

  1. Artificial Neural Network
  2. Fuzzy Logic
  3. Genetic algorithm
Artificial Neural Network

It is a connectionist modeling and parallel distributed network. There are of two types ANN (Artificial Neural Network) and BNN (Biological Neural Network). A neural network that processes a single element is known as a unit. The components of the unit are, input, weight, processing element, output. It is similar to our human neural system. The main advantage is that they solve the problems in parallel, artificial neural networks use electrical signals to communicate. But the main disadvantage is that they are not fault-tolerant that is if anyone of artificial neurons gets damaged it will not function anymore.

 An example of a handwritten character, where a character is written in Hindi by many people, they may write the same character but in a different form. As shown below, whichever way they write we can understand the character, because one already knows how the character looks like. This concept can be compared to our neural network system.

Fuzzy Logic

The fuzzy logic algorithm is used to solve the models which are based on logical reasoning like imprecise and vague. It was introduced by Latzi A. Zadeh in 1965. Fuzzy logic provides stipulated truth value with the closed interval [0,1]. Where 0 = false value, 1= true value.

An example of a robot that wants to move from one place to another within a short time where there are many obstacles on the way. Now the question arises is that how the robot can calculate its movement to reach the destination point, without colliding to any obstacle. These types of problems have uncertainty problem which can be solved using fuzzy logic.

Genetic Algorithm in Soft Computing

The genetic algorithm was introduced by Prof. John Holland in 1965. It is used to solve problems based on principles of natural selection, that come under evolutionary algorithm. They are usually used for optimization problems like maximization and minimization of objective functions, which are of two types of an ant colony and swarm particle. It follows biological processes like genetics and evolution.

The genetic algorithm can solve the problems which cannot be solved in real-time also known as the NP-Hard problem. The complicated problems which cannot be solved mathematically can be easily solved by applying the genetic algorithm. It is a heuristic search or randomized search method, which provides an initial set of solutions and generate a solution to the problem efficiently and effectively.

A simple way of understanding this algorithm is by considering the following example of a person who wants to invest some money in the bank, we know there are different banks available with different schemes and policies. Its individual interest how much amount to be invested in the bank, so that he can get maximum profit. There are certain criteria for the person that is, how he can invest and how can he get profited by investing in the bank. These criteria can be overcome by the “Evolutional Computing” algorithm like genetic computing.

Sunday, September 21, 2025

What is Edge Computing ?

 EDGE COMPUTING

Edge computing is a distributed computing model that moves data processing and storage closer to the devices where the data is generated, rather than relying on a centralized cloud or data center. By processing data at the "edge" of the network, this technology enables real-time responses, reduces latency, minimizes network bandwidth usage, and improves application performance for latency-sensitive applications like autonomous vehicles and industrial automation.  
 

Why is edge computing important?

Edge computing is becoming more popular because it allows enterprises to collect and analyze their raw data more efficiently. More than ever, organizations need instant access to their data to make informed decisions about their operational efficiency and business functions. When appropriately used, edge computing has the potential to help organizations improve safety and performance, automate processes, and improve user experience.

Here are some benefits of edge computing.

Reduced latency/increased speed
In many industries, technology demands almost instant transfer of data. Take the example of a piece of robotic machinery on a factory floor. If a production incident makes it unsafe for that robot to keep operating, it needs to receive that information as fast as possible so it can shut down.

Improved data security
With edge computing, the majority of data is processed and stored locally. Any information that needs to be sent back to the data center can be encrypted before transmission. Enterprises also use edge computing to comply with data sovereignty laws, such as the General Data Protection Regulation (GDPR), by keeping any sensitive data close to the source.

 
Increased productivity
Enterprises improve operational and employee productivity by responding more quickly to information. By analyzing data collected at the source, organizations can improve areas of their facilities, infrastructure, or equipment that are underperforming. Edge computing can be teamed with artificial intelligence and machine learning tools to derive business intelligence and insights that helps employees and enterprises perform more productively. 

Remote data collection
It is challenging to collect data from places with unreliable connectivity and bandwidth. Establishing compute and data storage capabilities at the network edge helps enterprises collect and transmit data from distant oil fields, industrial zones, and offshore vessels. 

Reduced costs
Sending large quantities of data from its origin to centralized data centers is expensive because it requires more bandwidth. The edge computing model allows you to decrease the amount of data being sent from sites to data centers because end users only send critical data. Depending on how much data your business sends and processes, this could significantly save operating costs. 

Reliable performance
Edge computing often takes place in remote areas where internet connectivity is scarce. By setting up an edge computing environment, enterprises ensure that their operations reliably process, analyze, and store data. This significantly reduces the chances of suffering from operational downtime caused by network or connectivity disruption.

Which industries use edge computing?

The high speeds and low latency of data transfer, combined with the relative ease of installing edge devices, have seen edge computing widely used across industries. 
Here are some examples.

Manufacturing
The proliferation of Internet of Things (IoT) devices such as sensors and gateways has made edge computing systems prevalent in the manufacturing industry. Manufacturers utilize edge computing solutions to enable automation, collect data on-site, improve production efficiency, and allow rapid machine-to-machine communication.

Autonomous vehicles
Autonomous vehicles like self-driving cars are fitted with several IoT sensors that collect large amounts of data every second. They require real-time data processing for instant response and cannot rely on a remote server for split-second decision-making.
Additionally, autonomous vehicles interact more efficiently if they communicate with each other first, as opposed to sending data on weather conditions, traffic, accidents, or detours to a remote server. Edge computing is critical technology for ensuring their safety and ability to accurately judge road conditions.

Energy
Energy companies use edge computing to collect and store data on oil rigs, gas fields, wind turbines, and solar farms. Rig operators commonly deploy edge artificial intelligence to detect hazards and optimize and inspect their pipelines. Edge computing helps the industry improve operational efficiency, keep its workers safe, and forecast when maintenance work needs to be undertaken.

Wednesday, September 17, 2025

Plant Biotechnology

 Plant Biotechnology

Plant biotechnology is defined as the use of metabolic properties of living plants to enhance the quality and quantity of agricultural products, utilizing techniques such as plant tissue culture for diverse applications in pharmacy, biotechnology, and food technology.

Abstract

Biotechnology explores the metabolic properties of living organisms for the production of valuable products of a very different structural and organizational level. Plant serves as an important source of primary and secondary metabolites used in pharmacy, biotechnology, and food technology. Plant biotechnology has gained importance in the recent past for augmenting the quality and quantity of agricultural, horticultural, ornamental plants, and in manipulating the plants for improved agronomic performance. Plant tissue culture is the most popular technique of plant biotechnology, which has diverse applications in the various fields. To understand the basic facts related with plant in vitro studies it is worth acknowledging historical principles of plant tissue culture science, which takes its roots from ground-breaking research like discovery of cells followed by the propounding of cell theory. This chapter covers various major historical achievements such as the concept of cellular totipotency, which was inherent in cell theory and was further elaborated by Haberlandt in 1902. This historical account created the scope and development for plant tissue culture science such as research and production of transgenic plants and their products, which could be of use to mankind as food, medicine, and life-saving drugs.


Plant biotechnology can be defined as the use of tissue culture and genetic engineering techniques to produce genetically modified plants that exhibit new or improved desirable characteristics. The desirable characteristics include, among others, better yields, better quality, and greater resistance to adverse factors, including diseases, pests, and environmental conditions such as freezes, drought, and salinity. Plant biotechnology also makes possible the production in plants of useful proteins coded by microbial, animal, or human genes. Plant biotechnology has shown that all of these goals are attainable, at least in the kinds of plants on which they have been attempted. The number of crop, ornamental, and forest plants that have been modified genetically and released by university and industry scientists around the world is in the thousands and continues to grow.

There are numerous cases in which plant biotechnology is used successfully to produce crop plants that avoid or resist certain plant pathogens. Some plants have been rendered resistant to specific pathogens by genetically engineering (transforming) them with isolated specific genes that provide resistance against these pathogens. Transformed plants become resistant by coding for enzymes that mobilize other enzymes that carry out numerous defensive functions, such as breaking down the structural compounds of the pathogen. Several of the enzymes produce compounds in the plant that are toxic to or otherwise inhibit the growth and spread of the pathogen both through the plant and to other plants. Other plants have been transformed with animal (mouse) genes that code for antibodies (plantibodies) against a coat protein of the pathogen. Genetic engineering has been particularly effective in producing plants resistant to viruses by incorporating viral genes in the crop plants that code for virus coat protein, for altered movement protein, or by incorporating in the plant noncoding segments of virus nucleic acid or even segments of the nonsense strand of the virus nucleic acid. Many of these crop plants have been tested for resistance in the field with excellent results.

Practical examples of successful genetic engineering of disease-resistant plants include melon, squash, tomato, tobacco, and papaya crops that are protected from a variety of viral diseases. The success of genetically engineered papaya for resistance to papaya ringspot virus has saved the papaya as a crop in Hawaii and in the Far East (Fig. 1-40). Numerous other cases are still under development. For example, engineering tobacco with a chimeric transgene containing sequences from two different viruses (turnip mosaic and tomato spotted wilt) resulted in new plants resistant to both viruses. Similarly, engineering tomato plants with a truncated version of the gene coding for the DNA replicase of one of the very destructive geminiviruses resulted in plants resistant not only to the virus from which the transgene was obtained, but also to three other viruses. In other work, potato plants engineered with a chimeric gene encoding two insect proteins exhibiting antimicrobial activities showed significant resistance to the late blight oomycete and their tubers were protected in storage from infection by the soft rot-causing bacteria. In other work, raspberry plants engineered with the gene coding for the common plant polygalacturonase-inhibiting protein (PGIP) became resistant to the gray mold fungus Botrytis cinerea, although the transgene in raspberry, but not in other plants, is expressed only in immature green fruit.


In addition to helping us engineer plants resistant to disease, molecular biology and biotechnology have made possible the development and use of nontoxic chemical substances that, when applied to plants externally, stimulate the plants and elicit the activation of their natural defense mechanisms, i.e., activation of the localized defense mechanism (hypersensitive response) and systemic-aquired resistance (SAR). Two such chemical substances that have been proven effective and are used commercially are Actigard, where one application increases the plants' resistance against some bacterial and some fungal diseases for several weeks, and Messenger, derived from the fire blight bacterium gene coding for the protein harpin, which elicits a hypersensitive response and SAR in plants. Messenger, which also promotes plant growth, is effective against a variety of diseases of several crops, including strawberry, tomato, and cotton.

In transforming plants for disease resistance or for any other characteristic, it is necessary to modify their nucleic acid by adding genetic material from another plant or, rarely, from an animal or a pathogen. In most cases, these nucleic acids are or become active, producing in the plant compounds that may be toxic to pathogens or pests and, possibly, to humans. In addition, some of this nucleic acid may find its way, through cross-pollination or through transfer by microorganisms, into weeds or other wild plants, making these plants also resistant to the pathogen or pest. Several kinds of plants have been engineered to produce toxins against certain insects; to produce vaccines against certain human pathogens; to produce animal or human growth hormones; or to produce pharmaceutical compounds that can be used to treat diseases of humans and animals. The fear by some people that some or all of these products will get into the human diet or in the animal food chain and cause allergies and other adverse health effects has resulted in significant unfavorable publicity for such products and for biotechnology. That type of publicity has, in turn, led many large buyers to refuse to buy and use products produced by genetically modified organisms (GMO). Following the adverse publicity, several governments, especially in Europe, passed laws and raised barriers to the importation of products derived from genetically modified organisms.

In addition to the argument against introducing into crops, through genetic engineering, new proteins that may cause allergic reactions in some people, there have also been arguments against biotechnology because it takes possession of, patents, and monopolizes genetic material that was previously available and free to everybody; it replaces the numerous sustainable local varieties with a few genetically engineered ones, the seed of which the farmers must buy from large companies every year; it threatens the development of pests and pathogens that can resist or overcome the transformed resistant crops; it threatens to lead to the use of larger amounts of herbicides with crops like those made herbicide resistant while the weeds are still susceptible; it threatens unknown numbers of nontarget organisms that may be affected adversely by the protein; it threatens to upset the plant balance, and through it the entire biotic balance of the environment, by having such new genes transferred naturally to nontarget plants and their proteins, harmless or not, consumed by microorganisms, animals, and humans unaccustomed to such proteins; it threatens the occurrence of accidents in which crops transformed for the production of pharmaceuticals, vaccines, and so on become mixed with edible crops.

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