Lion algorithm

Lion algorithm (LA) is one among the bio-inspired (or) nature-inspired optimization algorithms (or) that are mainly based on meta-heuristic principles. It was first introduced by B. R. Rajakumar in 2012 in the name, Lion’s Algorithm. It was further extended in 2014 to solve the system identification problem. This version was referred as LA, which has been applied by many researchers for their optimization problems. == Inspiration from lion’s social behaviour == Lions form a social system called a "pride", which consists of 1–3 pair of lions. A pride of lions shares a common area known as territory in which a dominant lion is called as territorial lion. The territorial lion safeguards its territory from outside attackers, especially nomadic lions. This process is called territorial defense. It protects the cubs till they become sexually matured. The maturity period is about 2–4 years. The pride undergoes survival fights to protect its territory and the cubs from nomadic lions. Upon getting defeated by the nomadic lions, the dominating nomadic lion takes the role of territorial lion by killing or driving out the cubs of the pride. The lioness of the pride give birth to cubs though the new territorial lion. When the cubs of the pride mature and considered to be stronger than the territorial lion, they take over the pride. This process is called territorial take-over. If territorial take-over happens, either the old territorial lion, which is considered to be laggard, is driven out or it leaves the pride. The stronger lions and lioness form the new pride and give birth to their own cubs == Terminology == In the LA, the terms that are associated with lion’s social system are mapped to the terminology of optimization problems. Few of such notable terms are related here. Lion: A potential solution to be generated or determined as optimal (or) near-optimal solution of the problem. The lion can be a territorial lion and lioness, cubs and nomadic lions that represent the solution based on the processing steps of the LA. Territorial lion: The strongest solution of the pride that tends to meet the objective function. Nomadic lion: A random solution, sometimes termed as nomad, to facilitate the exploration principle Laggard lion: Poor solutions that are failed in the survival fight. Pride: A pool of potential solutions i.e. a lion, lioness and their cubs, that are potential solutions of the search problem. Fertility evaluation: A process of evaluating whether the territorial lion and lioness are able to provide potential solutions in the future generations i.e. It ensures that the lion or lioness converge at every generation. Survival fight: It is a greedy selection process, which is often carried out between the pride and nomadic lion. == Algorithm == The steps involved in LA are given below: Pride Generation: Generate X m a l e {\displaystyle X^{male}} , X f e m a l e {\displaystyle X^{female}} and X 1 n o m a d {\displaystyle X_{1}^{nomad}} Determine f ( X m a l e ) {\displaystyle f(X^{male})} , f ( X f e m a l e ) {\displaystyle f(X^{female})} , f ( X 1 n o m a d ) {\displaystyle f(X_{1}^{nomad})} Initialize f r e f {\displaystyle f^{ref}} as f ( X m a l e ) {\displaystyle f(X^{male})} and N g {\displaystyle N_{g}} as 0 Memorize X m a l e {\displaystyle X^{male}} and X f e m a l e {\displaystyle X^{female}} Apply Fertility evaluation Process Generation of cubpool by mating Gender clustering: Define X c u b m a l e {\displaystyle X_{cub}^{male}} and X c u b f e m a l e {\displaystyle X_{cub}^{female}} Initialize a g e c u b {\displaystyle age_{cub}} as zero Apply Cub growth function Territorial defense: If X m a l e {\displaystyle X^{male}} (or pride) fails in the survival fight i.e. X 1 n o m a d {\displaystyle X_{1}^{nomad}} defeats the pride, go to step 4, else continue Increase a g e c u b {\displaystyle age_{cub}} by 1 and check whether cub attains maturity i.e., if a g e c u b > a g e m a x {\displaystyle age_{cub}>age_{max}} , go to Step 9, else continue Territorial takeover: If X c u b m a l e {\displaystyle X_{cub}^{male}} and X c u b f e m a l e {\displaystyle X_{cub}^{female}} are found to be closer to optimal solution, update X m a l e {\displaystyle X^{male}} and X f e m a l e {\displaystyle X^{female}} Increment N g {\displaystyle N_{g}} by 1 Repeat from Step 5, if termination criterion is not violated, else return X m a l e {\displaystyle X^{male}} as the near-optimal solution == Variants == The LA has been further taken forward to adopt in different problem areas. According to the characteristics of the problem area, significant amendment has been done in the processes and the models used in the LA. Accordingly, diverse variants have been developed by the researchers. They can be broadly grouped as hybrid LAs and non-hybrid LAs. Hybrid LAs are the LAs that are amended by the principle of other meta-heuristics, whereas the Non-hybrid LAs take any scientific amendment inside its operation that are felt to be essential to attend the respective problem area. == Applications == LA is applied in diverse engineering applications that range from network security, text mining, image processing, electrical systems, data mining and many more. Few of the notable applications are discussed here. Networking applications: In WSN, LA is used to solve the cluster head selection problem by determining optimal cluster head. Route discovery problem in both the VANET and MANET are also addressed by the LA in the literature. It is also used to detect attacks in advanced networking scenarios such as Software-Defined Networks (SDN) Power Systems: LA has attended generation rescheduling problem in a deregulated environment, optimal localization and sizing of FACTS devices for power quality enhancement and load-frequency controlling problem Cloud computing: LA is used in optimal container-resource allocation problem in cloud environment and cloud security

Virtual intelligence

Virtual intelligence (VI) is the term given to artificial intelligence that exists within a virtual world. Many virtual worlds have options for persistent avatars that provide information, training, role-playing, and social interactions. The immersion in virtual worlds provides a platform for VI beyond the traditional paradigm of past user interfaces (UIs). What Alan Turing established as a benchmark for telling the difference between human and computerized intelligence was devoid of visual influences. With today's VI bots, virtual intelligence has evolved past the constraints of past testing into a new level of the machine's ability to demonstrate intelligence. The immersive features of these environments provide nonverbal elements that affect the realism provided by virtually intelligent agents. Virtual intelligence is the intersection of these two technologies: Virtual environments: Immersive 3D spaces provide for collaboration, simulations, and role-playing interactions for training. Many of these virtual environments are currently being used for government and academic projects, including Second Life, VastPark, Olive, OpenSim, Outerra, Oracle's Open Wonderland, Duke University's Open Cobalt, and many others. Some of the commercial virtual worlds are also taking this technology into new directions, including the high-definition virtual world Blue Mars. Artificial intelligence (AI): AI is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. VI is a type of AI that operates within virtual environments to simulate human-like interactions and responses. == Applications == Cutlass Bomb Disposal Robot: Northrop Grumman developed a virtual training opportunity because of the prohibitive real-world cost and dangers associated with bomb disposal. By replicating a complicated system without having to learn advanced code, the virtual robot has no risk of damage, trainee safety hazards, or accessibility constraints. MyCyberTwin: NASA is among the companies that have used the MyCyberTwin AI technologies. They used it for the Phoenix rover in the virtual world Second Life. Their MyCyberTwin used a programmed profile to relay information about what the Phoenix rover was doing and its purpose. Second China: The University of Florida developed the "Second China" project as an immersive training experience for learning how to interact with the culture and language in a foreign country. Students are immersed in an environment that provides role-playing challenges coupled with language and cultural sensitivities magnified during country-level diplomatic missions or during times of potential conflict or regional destabilization. The virtual training provides participants with opportunities to access information, take part in guided learning scenarios, communicate, collaborate, and role-play. While China was the country for the prototype, this model can be modified for use with any culture to help better understand social and cultural interactions and see how other people think and what their actions imply. Duke School of Nursing Training Simulation: Extreme Reality developed virtual training to test critical thinking with a nurse performing trained procedures to identify critical data to make decisions and performing the correct steps for intervention. Bots are programmed to respond to the nurse's actions as the patient with their conditions improving if the nurse performs the correct actions.

Agent-assisted automation

Agent-assisted automation is a type of call center technology that automates elements of what the call center agent 1) does with his/her desktop tools and/or 2) says to customers during the call using pre-recorded audio. It is a relatively new category of call center technology that shows promise in improving call center productivity and compliance. == Types of agent-assisted automation == === Pre-recorded audio === Pre-recorded audio (sometimes referred to as soundboard (computer program) or as soundboard technology) is another form of agent-assisted automation. The purpose of using pre-recorded messages is to increase the probability (and in some cases error-proof the process so) that the right information is provided to customers at the right time. The required disclosures are pre-recorded to ensure accuracy and understandability. By integrating the recordings with the customer relationship management software, the right combination of disclosures can be played based on the combination of goods and services the customer purchased. The integration with the customer relationship management software also ensures that the order cannot be submitted until the disclosures are played, essentially error-proofing (poka-yoke) the process of ensuring the customer gets all the required consumer protection information. Phone surveys are ideal applications of this technology. Whether surveying market preferences or political views, the pre-recorded audio with an agent listening allows the questions to be asked in the same way every time, uninfluenced by the agents' fatigue levels, accents, or their own views. === Fraud prevention === Fraud prevention is a specialized type of agent-assisted automation focused on reducing ID theft and credit card fraud. ID theft and credit card fraud are huge threats for call centers and their customers and few good solutions exist, but new agent-assisted automation solutions are producing promising results. The technology allows the agents to remain on the phone while the customers use their phone key pads to enter the information. The tones are masked and the information passes directly into the customer relationship management system or payment gateway in the case of credit card transactions. The automation essentially makes it impossible for call center agents and also call center personnel that might be monitoring the calls to steal the credit card number, social security number, or other personally identifiable information. === Outbound telemarketing === Another specialized application space of agent-assisted automation is in outbound telemarketing, which goes under numerous headings including outbound prospecting, cold calling, solicitation, fund-raising, etc. Turnover is high among agents engaged in this kind of work because the task is tedious and emotionally difficult. It is tedious because the agent spends the bulk of their day, not talking to qualified leads, but in getting wrong numbers and answering machines. == Benefits == Just as automation has benefited manufacturing by reducing the mental and physical effort required of workers while simultaneously improving throughput, quality, and safety, agent-assisted automation is improving call center results while reducing the tiring aspects of the job for agents. In some cases, the agent-assisted automation streamlines the process and allows calls to be handled more quickly. By eliminating cutting and pasting from one application to another, by auto-navigating applications, and by providing a single view of the customer, agent-assisted automation can reduce call handle time and increase agent productivity. Second, in theory, the more steps that can be automated and the more logic that can be built into the call flow (e.g., if the customer buys items 2 and 9, then disclosures a, c, and f are read by the pre-recorded audio), then companies may be able to reduce the amount of training that is required of the agents while at the same time ensuring more consistency and accuracy. However, no published studies have reported this result yet. But an even larger problem in call centers is between-agent variation in behavior and results. Agents differ in the amount of training and coaching they receive, they differ in the amount of experience they have, their jobs are repetitious and tiring, and the process and procedures the agents are supposed to follow constantly change. Moreover, there are significant individual differences between agents in their intelligence, personality, motivations, etc. which all affect performance. Despite the large amount of money call centers have spent over decades trying to reduce between-agent variation, the problem is still so prevalent that one large study of customer interactions with call centers found that a customer's experience was completely a function of the quality of the agent who happened to answer the phone. Therefore, the most significant benefit of agent-assisted automation may prove to be in how the automation error-proofs or poka-yoke the process and ensures that something that needs to be done or said happens every time. Properly implemented, the between-agent variation for whatever step of the process the automation is applied to may be able to be reduced to near zero. This is especially important in a collection agency whose processes and procedures are closely regulated by the Fair Debt Collection Practices Act.

PhotoLine

PhotoLine is a general purpose bitmap and vector graphics editor developed and published by Computerinsel GmbH for Windows, macOS, and Linux/Wine. It was originally created in 1995 by Gerhard Huber and Martin Huber. The program combines bitmap and vector graphics editing in one seamless working application unlike most graphics software which tend to focus on either bitmap or vector editing and output. PhotoLine is considered as a market competitor to Adobe Photoshop. == Features == PhotoLine edits and composes multi-layer raster and vector images with deep support for masking and alpha compositing and with full color management. Editing and color management in PhotoLine is mostly non-destructive. Image data in layers is preserved without loss of information regardless of the document's image mode or layer transformation. color depth, image resolution, color model, and ICC profile are preserved for each individual layer or group of layers. Layers can be cloned and reused anywhere in the layer stack, including repurposed as layer masks. Layer blending and compositing in PhotoLine supports common blend modes, and features a layer blend range of -200 to +200 percent. It is also possible to control which channels are blended for each layer, adjustment layer, and layer mask or group of layers. Filters, adjustment layers, and brushes have access to Lab and HIS color modes (HIS is a variant of HSL), separately of the color model of the underlying image layer. In Addition to raster and vector editing, PhotoLine can be used for small desktop publishing projects. Multi-page documents with page spreads and text flow between text frames and pages are supported. Character and paragraph styles can be defined. Spot colors, bleed settings, a baseline grid, a table of contents generator, and PDF/X support help with these projects. PhotoLine is however much more limited when compared to dedicated publishing software such as Adobe InDesign or QuarkXPress. PhotoLine incorporates the Open-source software library LibRaw to read raw images from digital cameras for import. Developing these files is non-destructive with a choice of embedding the RAW image data either in the PhotoLine document or link to the external RAW image file. PhotoLine can open raw files as linear unmodified and non color managed source images. Photoshop PSD files can be imported and exported. Core functionality of PhotoLine can be extended through standard Photoshop filter plugins, the G'MIC digital image processing framework, and PSP tubes. External programs can be linked for a seamless round-trip workflow and files can be sent directly for processing in third-party design applications. Custom functionality is further supported through scripting and macro recording. == Early history == Developed by two brothers, Gerhard Huber and Martin Huber, PhotoLine was first released in January 1996 on the Atari ST line of personal computers from Atari Corporation. Previously, Gerhard and Martin had worked on making graphics cards for Atari computers and writing drivers for image scanners. Atari's market share was declining, and the brothers considered developing a video game to expand the business. This led them to search for image editing software that would run on Atari computers and fit their game project. Only an image editor called tms Cranach came close to what Gerhard and Martin had in mind. tms Cranach was a Raster graphics editor running on Atari's MegaST/STe, TT030, and Falcon030 systems. However, Cranach turned out to be expensive software and complicated to use. The brothers contacted tms (Cranach's developers) and this resulted in an offer from tms to purchase Cranach and its source code, as tms intended to exit the Atari software market. After the purchase of Cranach and its source code Gerhard and Martin initially continued to sell Cranach, but sales were low. In 1995 the two decided to start developing a new graphics editor called "PhotoLine". PhotoLine was developed from scratch and written in C++. It nevertheless contained a lot of know-how from Cranach (which was written in C). PhotoLine first release was launched one year later in 1996. With the growing popularity of Microsoft Windows, the release of Windows 95, and the limiting graphics hardware on the Atari platforms, the developers switched development platforms and continued development of PhotoLine for Windows only. The first Windows version (PhotoLine 2.2) was released in the middle of 1997. Shortly after, the Atari version was discontinued and saw its final release as PhotoLine 2.30. The Huber brothers released this final Atari version into the public domain in 2012. The first Classic Mac OS version of PhotoLine 6 appeared in 1999 after many ex-Atari users who had switched to Mac OS pressured the PhotoLine developers to release an Apple port. == Linux Support == PhotoLine runs natively under Windows and MacOS. While a native Linux version of PhotoLine is not available, running PhotoLine under Wine is actively supported and maintained by the developers. Running PhotoLine under Linux/Wine PhotoLine enables the user to allow Little CMS to fully support color management under Linux instead of the native OS CMS. == File format == Native PhotoLine files have the extension .PLD, which is an abbreviation of "PhotoLine Document". It can contain embedded JPEG, PNG, or camera raw images. It contains a preview image in JPEG or PNG format, which is used by the operating system or third-party applications to display a thumbnail of its contents. Thumbnails are natively supported on MacOS X. During installation on Windows the user is presented with an option to install a PLD thumbnail preview driver which enables thumbnails of PLD content in Windows Explorer. Alternatively, the FastPictureViewer Standalone Codec Pack provides the ability to display PLD thumbnails in Windows Explorer. == Version History == PhotoLine was first developed for the Atari ST computer. Version 2 was the first version for Windows, and since version 6 PhotoLine is also available for MacOS.

Audio-visual speech recognition

Audio visual speech recognition (AVSR) is a technique that uses image processing capabilities in lip reading to aid speech recognition systems in recognizing indeterministic phones or giving preponderance among near probability decisions. Each system of lip reading and speech recognition works separately, then their results are mixed at the stage of feature fusion. As the name suggests, it has two parts. First one is the audio part and second one is the visual part. In audio part we use features like log mel spectrogram, mfcc etc. from the raw audio samples and we build a model to get feature vector out of it . For visual part generally we use some variant of convolutional neural network to compress the image to a feature vector after that we concatenate these two vectors (audio and visual ) and try to predict the target object.

Biorobotics

Biorobotics is an interdisciplinary science that combines the fields of biomedical engineering, cybernetics, and robotics to develop new technologies that integrate biology with mechanical systems to develop more efficient communication, alter genetic information, and create machines that imitate biological systems. == Cybernetics == Cybernetics focuses on the communication and system of living organisms and machines that can be applied and combined with multiple fields of study such as biology, mathematics, computer science, engineering, and much more. This discipline falls under the branch of biorobotics because of its combined field of study between biological bodies and mechanical systems. Studying these two systems allows for advanced analysis on the functions and processes of each system as well as the interactions between them. === History === Cybernetic theory is a concept that has existed for centuries, dating back to the era of Plato where he applied the term to refer to the "governance of people". The term cybernetique is seen in the mid-1800s used by physicist André-Marie Ampère. The term cybernetics was popularized in the late 1940s to refer to a discipline that touched on, but was separate, from established disciplines, such as electrical engineering, mathematics, and biology. === Science === Cybernetics is often misunderstood because of the breadth of disciplines it covers. In the early 20th century, it was coined as an interdisciplinary field of study that combines biology, science, network theory, and engineering. Today, it covers all scientific fields with system related processes. The goal of cybernetics is to analyze systems and processes of any system or systems in an attempt to make them more efficient and effective. === Applications === Cybernetics is used as an umbrella term so applications extend to all systems related scientific fields such as biology, mathematics, computer science, engineering, management, psychology, sociology, art, and more. Cybernetics is used amongst several fields to discover principles of systems, adaptation of organisms, information analysis and much more. == Genetic engineering == Genetic engineering is a field that uses advances in technology to modify biological organisms. Through different methods, scientists are able to alter the genetic material of microorganisms, plants and animals to provide them with desirable traits. For example, making plants grow bigger, better, and faster. Genetic engineering is included in biorobotics because it uses new technologies to alter biology and change an organism's DNA for their and society's benefit. === History === Although humans have modified genetic material of animals and plants through artificial selection for millennia (such as the genetic mutations that developed teosinte into corn and wolves into dogs), genetic engineering refers to the deliberate alteration or insertion of specific genes to an organism's DNA. The first successful case of genetic engineering occurred in 1973 when Herbert Boyer and Stanley Cohen were able to transfer a gene with antibiotic resistance to a bacterium. === Science === There are three main techniques used in genetic engineering: The plasmid method, the vector method and the biolistic method. ==== Plasmid method ==== This technique is used mainly for microorganisms such as bacteria. Through this method, DNA molecules called plasmids are extracted from bacteria and placed in a lab where restriction enzymes break them down. As the enzymes do this, some develop a rough edge that resembles that of a staircase which is considered 'sticky' and capable of reconnecting. These 'sticky' molecules are inserted into another bacteria where they will connect to the DNA rings with the altered genetic material. ==== Vector method ==== The vector method is considered a more precise technique than the plasmid method as it involves the transfer of a specific gene instead of a whole sequence. In the vector method, a specific gene from a DNA strand is isolated through restriction enzymes in a laboratory and is inserted into a vector. Once the vector accepts the genetic code, it is inserted into the host cell where the DNA will be transferred. ==== Biolistic method ==== The biolistic method is typically used to alter the genetic material of plants. This method embeds the desired DNA with a metallic particle such as gold or tungsten in a high speed gun. The particle is then bombarded into the plant. Due to the high velocities and the vacuum generated during bombardment, the particle is able to penetrate the cell wall and inserts the new DNA into the cell. === Applications === Genetic engineering has many uses in the fields of medicine, research and agriculture. In the medical field, genetically modified bacteria are used to produce drugs such as insulin, human growth hormones and vaccines. In research, scientists genetically modify organisms to observe physical and behavioral changes to understand the function of specific genes. In agriculture, genetic engineering is extremely important as it is used by farmers to grow crops that are resistant to herbicides and to insects such as BTCorn. == Bionics == Bionics is a medical engineering field and a branch of biorobotics consisting of electrical and mechanical systems that imitate biological systems, such as prosthetics and hearing aids. It's a portmanteau that combines biology and electronics. === History === The history of bionics goes as far back in time as ancient Egypt. A prosthetic toe made out of wood and leather was found on the foot of a mummy. The time period of the mummy corpse was estimated to be from around the fifteenth century B.C. Bionics can also be witnessed in ancient Greece and Rome. Prosthetic legs and arms were made for amputee soldiers. In the early 16th century, a French military surgeon by the name of Ambroise Pare became a pioneer in the field of bionics. He was known for making various types of upper and lower prosthetics. One of his most famous prosthetics, Le Petit Lorrain, was a mechanical hand operated by catches and springs. During the early 19th century, Alessandro Volta further progressed bionics. He set the foundation for the creation of hearing aids with his experiments. He found that electrical stimulation could restore hearing by inserting an electrical implant to the saccular nerve of a patient's ear. In 1945, the National Academy of Sciences created the Artificial Limb Program, which focused on improving prosthetics since there were a large number of World War II amputee soldiers. Since this creation, prosthetic materials, computer design methods, and surgical procedures have improved, creating modern-day bionics. === Science === ==== Prosthetics ==== The important components that make up modern-day prosthetics are the pylon, the socket, and the suspension system. The pylon is the internal frame of the prosthetic that is made up of metal rods or carbon-fiber composites. The socket is the part of the prosthetic that connects the prosthetic to the person's missing limb. The socket consists of a soft liner that makes the fit comfortable, but also snug enough to stay on the limb. The suspension system is important in keeping the prosthetic on the limb. The suspension system is usually a harness system made up of straps, belts or sleeves that are used to keep the limb attached. The operation of a prosthetic could be designed in various ways. The prosthetic could be body-powered, externally-powered, or myoelectrically powered. Body-powered prosthetics consist of cables attached to a strap or harness, which is placed on the person's functional shoulder, allowing the person to manipulate and control the prosthetic as he or she deems fit. Externally-powered prosthetics consist of motors to power the prosthetic and buttons and switches to control the prosthetic. Myoelectrically powered prosthetics are new, advanced forms of prosthetics where electrodes are placed on the muscles above the limb. The electrodes will detect the muscle contractions and send electrical signals to the prosthetic to move the prosthetic. The downside to this type of prosthetic is that if the sensors are not placed correctly on the limb then the electrical impulses will fail to move the prosthetic. TrueLimb is a specific brand of prosthetics that uses myoelectrical sensors which enable a person to have control of their bionic limb. ==== Hearing aids ==== Four major components make up the hearing aid: the microphone, the amplifier, the receiver, and the battery. The microphone takes in outside sound, turns that sound to electrical signals, and sends those signals to the amplifier. The amplifier increases the sound and sends that sound to the receiver. The receiver changes the electrical signal back into sound and sends the sound into the ear. Hair cells in the ear will sense the vibrations from the sound, convert the vibrations into nerve signals, and send it to the brain so

Virtual intelligence

Agent-assisted automation

PhotoLine

Audio-visual speech recognition

Biorobotics

PhotoLine

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